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 Showing 601 - 538 of 538 Journals sorted alphabetically Results in Mathematics Results in Nonlinear Analysis Review of Symbolic Logic       (Followers: 2) Reviews in Mathematical Physics       (Followers: 1) Revista Baiana de Educação Matemática Revista Bases de la Ciencia Revista BoEM - Boletim online de Educação Matemática Revista Colombiana de Matemáticas       (Followers: 1) Revista de Ciencias Revista de Educación Matemática Revista de la Escuela de Perfeccionamiento en Investigación Operativa Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas Revista de Matemática : Teoría y Aplicaciones       (Followers: 1) Revista Digital: Matemática, Educación e Internet Revista Electrónica de Conocimientos, Saberes y Prácticas Revista Integración : Temas de Matemáticas Revista Internacional de Sistemas Revista Latinoamericana de Etnomatemática Revista Latinoamericana de Investigación en Matemática Educativa Revista Matemática Complutense Revista REAMEC : Rede Amazônica de Educação em Ciências e Matemática Revista SIGMA Ricerche di Matematica RMS : Research in Mathematics & Statistics Royal Society Open Science       (Followers: 7) Russian Journal of Mathematical Physics Russian Mathematics Sahand Communications in Mathematical Analysis Sampling Theory, Signal Processing, and Data Analysis São Paulo Journal of Mathematical Sciences Science China Mathematics       (Followers: 1) Science Progress       (Followers: 1) Sciences & Technologie A : sciences exactes Selecta Mathematica       (Followers: 1) SeMA Journal Semigroup Forum       (Followers: 1) Set-Valued and Variational Analysis SIAM Journal on Applied Mathematics       (Followers: 11) SIAM Journal on Computing       (Followers: 11) SIAM Journal on Control and Optimization       (Followers: 18) SIAM Journal on Discrete Mathematics       (Followers: 8) SIAM Journal on Financial Mathematics       (Followers: 3) SIAM Journal on Mathematics of Data Science       (Followers: 1) SIAM Journal on Matrix Analysis and Applications       (Followers: 3) SIAM Journal on Optimization       (Followers: 12) Siberian Advances in Mathematics Siberian Mathematical Journal Sigmae SILICON SN Partial Differential Equations and Applications Soft Computing       (Followers: 7) Statistics and Computing       (Followers: 13) Stochastic Analysis and Applications       (Followers: 2) Stochastic Partial Differential Equations : Analysis and Computations       (Followers: 1) Stochastic Processes and their Applications       (Followers: 5) Stochastics and Dynamics Studia Scientiarum Mathematicarum Hungarica       (Followers: 1) Studia Universitatis Babeș-Bolyai Informatica Studies In Applied Mathematics       (Followers: 1) Studies in Mathematical Sciences       (Followers: 1) Superficies y vacio Suska Journal of Mathematics Education       (Followers: 1) Swiss Journal of Geosciences       (Followers: 1) Synthesis Lectures on Algorithms and Software in Engineering       (Followers: 2) Synthesis Lectures on Mathematics and Statistics       (Followers: 1) Tamkang Journal of Mathematics Tatra Mountains Mathematical Publications Teaching Mathematics       (Followers: 10) Teaching Mathematics and its Applications: An International Journal of the IMA       (Followers: 4) Teaching Statistics       (Followers: 8) Technometrics       (Followers: 8) The Journal of Supercomputing       (Followers: 1) The Mathematica journal The Mathematical Gazette       (Followers: 1) The Mathematical Intelligencer The Ramanujan Journal The VLDB Journal       (Followers: 2) Theoretical and Mathematical Physics       (Followers: 7) Theory and Applications of Graphs Topological Methods in Nonlinear Analysis Transactions of the London Mathematical Society       (Followers: 1) Transformation Groups Turkish Journal of Mathematics Ukrainian Mathematical Journal Uniciencia Uniform Distribution Theory Unisda Journal of Mathematics and Computer Science Unnes Journal of Mathematics       (Followers: 2) Unnes Journal of Mathematics Education       (Followers: 2) Unnes Journal of Mathematics Education Research       (Followers: 1) Ural Mathematical Journal Vestnik Samarskogo Gosudarstvennogo Tekhnicheskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki Vestnik St. Petersburg University: Mathematics VFAST Transactions on Mathematics       (Followers: 1) Vietnam Journal of Mathematics Vinculum Visnyk of V. N. Karazin Kharkiv National University. Ser. Mathematics, Applied Mathematics and Mechanics       (Followers: 1) Water SA       (Followers: 2) Water Waves Zamm-Zeitschrift Fuer Angewandte Mathematik Und Mechanik       (Followers: 1) ZDM       (Followers: 2) Zeitschrift für angewandte Mathematik und Physik       (Followers: 2) Zeitschrift fur Energiewirtschaft Zetetike

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Similar Journals
 SIAM Journal on Control and OptimizationJournal Prestige (SJR): 1.399 Citation Impact (citeScore): 2Number of Followers: 18      Hybrid journal (It can contain Open Access articles) ISSN (Print) 0363-0129 - ISSN (Online) 1095-7138 Published by Society for Industrial and Applied Mathematics  [17 journals]
• A Probabilistic Method for a Class of Non-Lipschitz BSDEs with Application
to Fund Management

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Authors: Jinhui Han, Sheung Chi Phillip Yam
Pages: 1193 - 1222
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 3, Page 1193-1222, June 2022.
The present work is devoted to a study of the solvability of a class of non-Lipschitz and noncanonical backward stochastic differential equations (BSDEs) that naturally arises from an intertemporal mutual fund management problem; to this end, we propose a method of combining the techniques of Malliavin calculus and a discussion on the Jacobian flow of the BSDE. Specifically, based on the intimate relationship between $Y_t$ and $Z_t$ of the BSDE via the Malliavin derivative of the former, namely, $D_tY_t=Z_t$, we construct an iterative Picard converging scheme for approximating the underlying solution pair by first obtaining $Z_t$ from the derived BSDE with respect to the Malliavin derivative and then recovering $Y_t$ from the underlying BSDE. A local unique existence result is first warranted over a short time horizon with carefully examined a priori estimates; indeed, each term in the iterative sequence is related to different Girsanov transforms for change of measure, and comparing them demands a delicate analysis. The use of Jacobian flow further enables us to properly control the lower and upper bounds for a certain product of the forward process and $Z_t$, which enables us to extend the solution globally by an inductive argument. Our proposed method is fundamentally different from other probabilistic methods that also involve estimating or bounding Malliavin traces such as [E. Pardoux and S. Peng, Some Backward SDEs with Non-Lipschitz Coefficients, Technical note, Université de Provence, Aix-en-Provence, France, 1996] and [M. C. Zedouri, Equations Différentielles Stochastiques Rétrogrades avec Générateurs Lipschitiziens Stochastiques, Master's Thesis, Université Mohammed Seddik Ben Yahia-Jijel, Jijel, Algeria, 2010]. We believe that our new approach proposed here can be potentially applied to resolve many other general non-Lipschitz forward-backward stochastic differential equations (FBSDEs) encountered in economics and finance, especially in the presence of generic utility functions.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-05-03T07:00:00Z
DOI: 10.1137/21M140609X
Issue No: Vol. 60, No. 3 (2022)

• Moment Dynamics and Observer Design for a Class of Quasilinear Quantum
Stochastic Systems

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Authors: Igor G. Vladimirov, Ian R. Petersen
Pages: 1223 - 1249
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 3, Page 1223-1249, June 2022.
This paper is concerned with a class of open quantum systems whose dynamic variables have an algebraic structure, similar to that of the Pauli matrices pertaining to finite-level systems. The system interacts with external bosonic fields, and its Hamiltonian and coupling operators depend linearly on the system variables. This results in a Hudson--Parthasarathy quantum stochastic differential equation (QSDE) whose drift and dispersion terms are affine and linear functions of the system variables. The quasilinearity of the QSDE leads to tractable dynamics of mean values and higher-order multipoint moments of the system variables driven by vacuum input fields. This allows for the closed-form computation of the quasi-characteristic function of the invariant quantum state of the system and infinite-horizon asymptotic growth rates for a class of cost functionals. The tractability of the moment dynamics is also used for mean square optimal Luenberger observer design in a measurement-based filtering problem for a quasilinear quantum plant, which leads to a Kalman-like quantum filter.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-05-05T07:00:00Z
DOI: 10.1137/20M1386529
Issue No: Vol. 60, No. 3 (2022)

• State-Dependent Temperature Control for Langevin Diffusions

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Authors: Xuefeng Gao, Zuo Quan Xu, Xun Yu Zhou
Pages: 1250 - 1268
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 3, Page 1250-1268, June 2022.
We study the temperature control problem for Langevin diffusions in the context of nonconvex optimization. The classical optimal control of such a problem is of the bang-bang type, which is overly sensitive to errors. A remedy is to allow the diffusions to explore other temperature values and hence smooth out the bang-bang control. We accomplish this by a stochastic relaxed control formulation incorporating randomization of the temperature control and regularizing its entropy. We derive a state-dependent, truncated exponential distribution, which can be used to sample temperatures in a Langevin algorithm, in terms of the solution to an Hamilton--Jacobi--Bellman partial differential equation. We carry out a numerical experiment on a one-dimensional baseline example, in which the Hamilton--Jacobi--Bellman equation can be easily solved, to compare the performance of the algorithm with three other available algorithms in search of a global optimum.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-05-16T07:00:00Z
DOI: 10.1137/21M1429424
Issue No: Vol. 60, No. 3 (2022)

• Optimal Dividend Strategies with Reinsurance under Contagious Systemic
Risk

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Authors: Ming Qiu, Zhuo Jin, Shuanming Li
Pages: 1269 - 1293
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 3, Page 1269-1293, June 2022.
This paper studies the multidimensional mixed singular-regular stochastic control problems subject to reduced-form default driven by contagious intensities. The dynamic process of surplus is given by a system of diffusion processes with two controls, and the intensity of the reduced-form model increases when defaults occur. We derive the recursive Hamilton--Jacobi--Bellman variational inequalities by the dynamic programming principle and present analytical and recursive solutions. We prove that the solutions are classical and recursively associated with each other by the default states. The verification theorem is presented.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-05-16T07:00:00Z
DOI: 10.1137/21M1422318
Issue No: Vol. 60, No. 3 (2022)

• Proportional Local Assignability of the Dichotomy Spectrum of One-Sided
Discrete Time-Varying Linear Systems

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Authors: Pham The Anh, Artur Babiarz, Adam Czornik, Thai Son Doan
Pages: 1294 - 1319
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 3, Page 1294-1319, June 2022.
We consider a problem of assignability of the dichotomy spectrum for one-sided discrete time-varying linear systems. Our purpose is to prove that uniform complete controllability is a sufficient condition for proportional local assignability of the dichotomy spectrum.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-05-19T07:00:00Z
DOI: 10.1137/21M1410932
Issue No: Vol. 60, No. 3 (2022)

• Finite-Time Stability of Polyhedral Sweeping Processes with Application to
Elastoplastic Systems

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Authors: Ivan Gudoshnikov, Oleg Makarenkov, Dmitrii Rachinskii
Pages: 1320 - 1346
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 3, Page 1320-1346, June 2022.
We use the ideas of Adly, Attouch, and Cabot [in Nonsmooth Mechanics and Analysis, Adv. Mech. Math. 12, Springer, New York, 2006, pp. 289--304] on finite-time stabilization of dry friction oscillators to establish a theorem on finite-time stabilization of differential inclusions with a moving polyhedral constraint (known as polyhedral sweeping processes) of the form $C+c(t)$. We then employ the ideas of Moreau [in New Variational Techniques in Mathematical Physics (Centro Internaz. Mat. Estivo (CIME), II Ciclo, Bressanone, 1973), Edizioni Cremonese, Rome, 1974, pp. 171--322] to apply our theorem to a system of elastoplastic springs with a displacement-controlled loading. We show that verifying the condition of the theorem ultimately leads to the following two problems: (i) identifying the active vertex “A” or the active face “A” of the polyhedron that the vector $c'(t)$ points at; (ii) computing the distance from $c'(t)$ to the normal cone to the polyhedron at “A.” We provide a computational guide for solving problems (i)--(ii) in the case of an arbitrary elastoplastic system and apply it to a particular example. Due to the simplicity of the particular example, we can solve (i)--(ii) by the methods of linear algebra and basic combinatorics.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-05-19T07:00:00Z
DOI: 10.1137/20M1388796
Issue No: Vol. 60, No. 3 (2022)

• Special Section on Mathematical Modeling, Analysis, and Control of
Epidemics

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Authors: Carolyn Beck, Francesco Bullo, Giacomo Como, Kimon Drakopoulos, Dang H. Nguyen, Cameron Nowzari, Victor M. Preciado, Shreyas Sundaram
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page Si-Sii, April 2022.
This special section of the SIAM Journal on Control and Optimization (SICON) addresses the fundamental challenges inherent in the mathematical modeling, analysis, and control of epidemics. The ongoing COVID-19 pandemic has brought into the spotlight the critical importance of understanding complex epidemic processes. Yet the modeling, analysis, estimation, and control of these processes presents several formidable challenges. Epidemics inherently exhibit nonlinear dynamics as they spread through populations, requiring analytical techniques that can accurately capture both the transient and the steady-state aspects of the disease. Furthermore, careful attention must be paid to the resolution at which to model the epidemics, ranging from coarse mass-action models to metapopulation and individual-level models. Each of these approaches provides different insights and challenges for analysis and control. Models that operate at finer resolutions will also come with an increase in complexity, necessitating tractable abstractions ranging from mean-field models to deterministic and stochastic ODEs and PDEs. In the context of estimation of epidemics, the key challenge is to determine the important parameters of the epidemic (often in real time) based on data gathered from the affected population, in conjunction with dynamical models of the spreading process. Finally, formulating techniques to optimally control and address an ongoing epidemic (through either a centralized intervention, decentralized incentive mechanisms, or resource allocation policies) remains a critical challenge. The special section gathers contributions from the intersection of the fields of systems and control theory and the mathematical study of epidemic spread processes. A total of 16 papers represents a wide range of topics in modeling, identification, dynamic analysis, control, and optimization. Regarding modeling problems, the section covers contributions on Polya contagion networks, networked bivirus epidemic models, and age-differentiated compartmental models. For dynamic analysis, the section highlights contributions on explicit solutions and control design for simplified models, convergence and equilibria analysis, as well as the role of delays and saturations in closed loop settings. For identification and estimation problems, contributions are offered on the identifiability of model parameters and on parameter estimation using limited measurements. Most of the contributed articles focus on control design, optimization, and game theoretic problems. Centralized and distributed strategies are proposed. Actuation mechanisms include edge deletion, test allocation, optimal incentives, optimal switching between lockdown and opening the economy, screening, and curing policies. We believe this special section presents an excellent cross section of current research and hope that it will be of great interest to the broad readership of SICON. We offer our deepest thanks to all authors who submitted their work and to all reviewers who contributed their time and energy to the peer-review process. We would also like to thank the proficient and timely editorial support provided by Brian Fauth and the insightful and gracious advice that we received from the SICON Editor-in-Chief George Yin. Carolyn Beck University of Illinois at Urbana-Champaign Francesco Bullo University of California, Santa Barbara Giacomo Como Politecnico di Torino Kimon Drakopoulos University of Southern California Dang H. Nguyen University of Alabama Cameron Nowzari George Mason University Victor M. Preciado University of Pennsylvania Shreyas Sundaram Purdue University, Guest editors
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-28T07:00:00Z
DOI: 10.1137/22N975470
Issue No: Vol. 60, No. 2 (2022)

• A Unification of Weighted and Unweighted Particle Filters

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Authors: Ehsan Abedi, Simone Carlo Surace, Jean-Pascal Pfister
Pages: 597 - 619
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 597-619, April 2022.
Particle filters (PFs), which are successful methods for approximating the solution of the filtering problem, can be divided into two types: weighted and unweighted PFs. It is well known that weighted PFs suffer from the weight degeneracy and curse of dimensionality. To sidestep these issues, unweighted PFs have been gaining attention, though they have their own challenges. The existing literature on these types of PFs is based on distinct approaches. In order to establish a connection, we put forward a framework that unifies weighted and unweighted PFs in the continuous-time filtering problem. We show that the stochastic dynamics of a particle system described by a pair process, representing particles and their importance weights, should satisfy two necessary conditions in order for its distribution to match the solution of the Kushner--Stratonovich equation. In particular, we demonstrate that the bootstrap particle filter (BPF), which relies on importance sampling, and the feedback particle filter (FPF), which is an unweighted PF based on optimal control, arise as special cases from a broad class and that there is a smooth transition between the two. The freedom in designing the PF dynamics opens up potential ways to address the existing issues in the aforementioned algorithms, namely weight degeneracy in the BPF and gain estimation in the FPF.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-01T08:00:00Z
DOI: 10.1137/20M1382404
Issue No: Vol. 60, No. 2 (2022)

• A Stochastic Model of Economic Growth in Time-Space

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Authors: Fausto Gozzi, Marta Leocata
Pages: 620 - 651
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 620-651, April 2022.
We deal with an infinite horizon, infinite dimensional stochastic optimal control problem arising in the study of economic growth in time-space. Such a problem has been the object of various papers in deterministic cases when the possible presence of stochastic disturbances is ignored (see, e.g., [P. Brito, The Dynamics of Growth and Distribution in a Spatially Heterogeneous World, working paper 2004/14, ISEG-Lisbon School of Economics and Management, University of Lisbon, 2004], [R. Boucekkine, C. Camacho, and G. Fabbri, J. Econom. Theory, 148 (2013), pp. 2719--2736], [G. Fabbri, J. Econom. Theory, 162 (2016), pp. 114--136], and [R. Boucekkine, G. Fabbri, S. Federico, and F. Gozzi, J. Econom. Geography, 19 (2019), pp. 1287--1318]). Here we propose and solve a stochastic generalization of such models where the stochastic term, in line with the standard stochastic economic growth models (see, e.g., the books [A. G. Malliaris and W. A. Brock, Stochastic Methods in Economics and Finance, Advanced Textbooks in Economics 17, North Holland, 1982, Chapter 3] and [H. Morimoto, Stochastic Control and Mathematical Modeling: Applications in Economics, Cambridge Books, 2010, Chapter 9]), is a multiplicative one, driven by a cylindrical Wiener process. The problem is studied using the dynamic programming approach. We find an explicit solution of the associated HJB equation, use a verification type result to prove that such a solution is the value function, and find the optimal feedback strategies. Finally, we use this result to study the asymptotic behavior of the optimal trajectories.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-01T08:00:00Z
DOI: 10.1137/21M1414206
Issue No: Vol. 60, No. 2 (2022)

• Constructive Exact Control of Semilinear 1D Wave Equations by a
Least-Squares Approach

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Authors: Arnaud Münch, Emmanuel Trélat
Pages: 652 - 673
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 652-673, April 2022.
It has been proved by Zuazua in the nineties that the internally controlled semilinear 1D wave equation $\partial_{tt}y-\partial_{xx}y + g(y)=f 1_{\omega}$, with Dirichlet boundary conditions, is exactly controllable in $H^1_0(0,1)\cap L^2(0,1)$ with controls $f\in L^2((0,1)\times(0,T))$, for any $T>0$ and any nonempty open subset $\omega$ of (0,1), assuming that $g\in \mathcal{C}^1(\mathbb{R})$ does not grow faster than $\beta\vert x\vert \ln^{2}\vert x\vert$ at infinity for some $\beta>0$ small enough. The proof, based on the Leray--Schauder fixed point theorem, is, however, not constructive. In this article, we design a constructive proof and algorithm for the exact controllability of semilinear 1D wave equations. Assuming that $g^\prime$ does not grow faster than $\beta \ln^{2}\vert x\vert$ at infinity for some $\beta>0$ small enough and that $g^\prime$ is uniformly Hölder continuous on $\mathbb{R}$ with exponent $s\in[0,1]$, we design a least-squares algorithm yielding an explicit sequence converging to a controlled solution for the semilinear equation, at least with order $1+s$ after a finite number of iterations.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-07T08:00:00Z
DOI: 10.1137/20M1380661
Issue No: Vol. 60, No. 2 (2022)

• A Constructive Approach to Existence of Equilibria in Time-Inconsistent
Stochastic Control Problems

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Authors: Jiang Yu Nguwi, Nicolas Privault
Pages: 674 - 698
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 674-698, April 2022.
We extend the construction of equilibria for linear-quadratic and mean-variance portfolio problems available in the literature to a large class of mean-field time-inconsistent stochastic control problems in continuous time. Our approach relies on a time discretization of the control problem via $n$-person games, which are characterized via the maximum principle using backward stochastic differential equations. The existence of equilibria is proved by applying weak convergence arguments to the solutions of $n$-person games. A numerical implementation is provided by approximating $n$-person games using finite Markov chains.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-09T08:00:00Z
DOI: 10.1137/20M1370446
Issue No: Vol. 60, No. 2 (2022)

• On the Continuous Finite-Time Stabilization of the Double Integrator

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Authors: Arturo Zavala-Río, Tonametl Sanchez, Griselda I. Zamora-Gómez
Pages: 699 - 719
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 699-719, April 2022.
Continuous finite-time stabilization is often treated under the analytical framework of homogeneity and has been frequently illustrated in the context of the feedback control of the double integrator. For such a simple system, the simplest considered continuous finite-time controller is composed of gained (proportional) exponentially weighted position and velocity error correction terms, with the exponential weights generally less than unity and constrained to satisfy a particular relation among them under homogeneity. What happens for less-than-unity exponential weights that do not satisfy such a homogeneity-based relation' Does the finite-time stabilization hold' Through a Lyapunov function--based study, we analyze and give more concrete answers to such questions than those partially provided by previous studies on the topic. We do find a more exhaustive spectrum of the exponential weights that give rise to finite-time stability of the trivial solution. Other types of stability properties are further found to take place for less-than-or-equal-to-unity exponential weights. Moreover, through complementary analysis, local or ultimate behavior of the system solutions is further characterized. The analytical findings are further illustrated through computer simulations.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-09T08:00:00Z
DOI: 10.1137/20M136459X
Issue No: Vol. 60, No. 2 (2022)

• Euclidean Distance Bounds for Linear Matrix Inequalities Analytic Centers
Using a Novel Bound on the Lambert Function

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Authors: Biel Roig-Solvas, Mario Sznaier
Pages: 720 - 731
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 720-731, April 2022.
Linear matrix inequalities (LMIs) are ubiquitous in modern control theory, as well as in a variety of other fields in science and engineering. Their analytic centers, i.e. the maximum determinant elements of the feasible set spanned by these LMIs, are the solution of many well-known problems in statistics, communications, geometry, and control and can be approximated to arbitrary precision by semidefinite programs (SDPs). The quality of these approximations is measured with respect to the difference in log-determinant of both the exact and the approximate solutions to these SDPs, a quantity that follows directly from the duality theory of semidefinite programming. However, in many applications the relevant parameters are functions of the entries of the LMI argument $X$. In these cases it is of interest to develop metrics that quantify the quality of approximate solutions based on the error of these parameters, something that the log-determinant error fails to capture due to the nonlinear interaction of all the matrix entries. In this work we develop upper bounds on the Frobenius norm error between suboptimal solutions $X_f$ and the exact optimizer $X_*$ of maximum determinant problems, a metric that provides a direct translation to the entrywise error of $X$ and thus to the relevant parameters of the application. We show that these bounds can be expressed through the use of the Lambert function $W(x)$, i.e., the solution of the equation $W(x) e^{W(x)}= x$, and derive novel bounds for one of its branches to generate efficient closed-form bounds on the Euclidean distance to the LMI analytic center. Finally, we test the quality of these bounds numerically in the context of interior point methods termination criteria.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-15T07:00:00Z
DOI: 10.1137/20M1349928
Issue No: Vol. 60, No. 2 (2022)

• Continuous Rendezvous Algorithm for Memoryless Agents with Limited
Visibility in the Euclidean Space

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Authors: Doheon Kim
Pages: 732 - 757
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 732-757, April 2022.
A continuous-in-time rendezvous algorithm for memoryless agents with limited visibility on the Euclidean plane was proposed in [N. Gordon, I. A. Wagner, and A. M. Bruckstein in Ant Colony Optimization and Swarm Intelligence, Springer, Berlin, 2004, pp. 142--153] and was formulated as a system of differential equations in [L. I. Bellaiche and A. Bruckstein, Swarm Intell., 11 (2017), pp. 271--293]. We generalize this algorithm by letting the agents move in the Euclidean space of arbitrary dimension. And we provide a rigorous existence theory for this algorithm, which was not done before this work, even for the original algorithm for agents on a plane. Finally, for dimension not greater than three, we show that rendezvous is achieved in finite time, which is robust with respect to the number of the agents.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-17T07:00:00Z
DOI: 10.1137/20M1387584
Issue No: Vol. 60, No. 2 (2022)

• A Class of Stochastic Games and Moving Free Boundary Problems

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Authors: Xin Guo, Wenpin Tang, Renyuan Xu
Pages: 758 - 785
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 758-785, April 2022.
In this paper we propose and analyze a class of $N$-player stochastic games that include finite fuel stochastic games as a special case. We first derive sufficient conditions for the Nash equilibrium (NE) in the form of a verification theorem. The associated quasi-variational-inequalities include an essential game component regarding the interactions among players, which may be interpreted as the analytical representation of the conditional optimality for NEs. The derivation of NEs involves solving first a multidimensional free boundary problem and then a Skorokhod problem. Finally, we present an intriguing connection between these NE strategies and controlled rank-dependent stochastic differential equations.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-17T07:00:00Z
DOI: 10.1137/20M1322558
Issue No: Vol. 60, No. 2 (2022)

• Feedforward Neural Networks and Compositional Functions with Applications
to Dynamical Systems

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Authors: Wei Kang, Qi Gong
Pages: 786 - 813
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 786-813, April 2022.
In this paper we develop an algebraic framework for analyzing neural network approximation of compositional functions, a rich class of functions that are frequently encountered in applications. The framework is developed in a way so that it supports the error analysis for not only functions as input-output relations, but also numerical algorithms. This capability is critical because it enables the analysis of neural network approximation errors for problems for which analytic solutions are not available, such as differential equations and optimal control. A set of key compositional features as well as its relationship with the complexity of neural network approximations are identified. We prove that in the approximation of functions, differential equations and optimal control, the complexity of neural networks is bounded by a polynomial function of the key features and error tolerance. The results shed light on the reason why using neural network approximations helps to avoid the curse of dimensionality.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-17T07:00:00Z
DOI: 10.1137/21M1391596
Issue No: Vol. 60, No. 2 (2022)

• Moment-Driven Predictive Control of Mean-Field Collective Dynamics

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Authors: Giacomo Albi, Michael Herty, Dante Kalise, Chiara Segala
Pages: 814 - 841
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 814-841, April 2022.
The synthesis of control laws for interacting agent-based dynamics and their mean-field limit is studied. A linearization-based approach is used for the computation of suboptimal feedback laws obtained from the solution of differential matrix Riccati equations. Quantification of dynamic performance of such control laws leads to theoretical estimates on suitable linearization points of the nonlinear dynamics. Subsequently, the feedback laws are embedded into a nonlinear model predictive control framework where the control is updated adaptively in time according to dynamic information on moments of linear mean-field dynamics. The performance and robustness of the proposed methodology is assessed through different numerical experiments in collective dynamics.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-24T07:00:00Z
DOI: 10.1137/21M1391559
Issue No: Vol. 60, No. 2 (2022)

• Robustness to Incorrect Priors and Controlled Filter Stability in
Partially Observed Stochastic Control

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Authors: Curtis McDonald, Serdar Yüksel
Pages: 842 - 870
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 842-870, April 2022.
We study controlled filter stability and its effects on the robustness properties of optimal control policies designed for systems with incorrect priors applied to a true system. Filter stability refers to the correction of an incorrectly initialized filter for a partially observed stochastic dynamical system (controlled or control-free) with increasing measurements. This problem has been studied extensively in the control-free context, and except for the standard machinery for linear Gaussian systems involving the Kalman filter, few studies exist for the controlled setup. One of the main differences between control-free and controlled partially observed Markov chains is that the filter is always Markovian under the former, whereas under a controlled model the filter process may not be Markovian since the control policy may depend on past measurements in an arbitrary (measurable) fashion. This complicates the dependency structure and therefore results from the control-free literature do not directly apply to the controlled setup. In this paper, we study the filter stability problem for controlled stochastic dynamical systems and provide sufficient conditions for when a falsely initialized filter merges with the correctly initialized filter over time. These stability results are applied to robust stochastic control problems: under filter stability, we bound the difference in the expected cost incurred for implementing an incorrectly designed control policy compared to an optimal policy. A conclusion is that filter stability leads to stronger robustness results to incorrect priors (compared with results in [A. D. Kara and S. Yüksel, SIAM J. Control Optim., 57 (2019), pp. 1929--1964] without controlled filter stability). Furthermore, if the optimum cost is the same for each prior, the cost of mismatch between the true prior and the assumed prior is zero.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-04T07:00:00Z
DOI: 10.1137/21M1417442
Issue No: Vol. 60, No. 2 (2022)

• On a Fractional Diffusion Equation with Moving Control

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Authors: Sorin Micu, Constantin Niţă
Pages: 871 - 889
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 871-889, April 2022.
This article considers a one-dimensional fractional parabolic equation, which may serve as a mathematical model for anomalous diffusion phenomena. It is known that, if the support of the control is fixed, this equation is not even spectrally controllable. We show that, when the control support is shifted in time with constant velocity $c$, the system is null-controllable in a sufficiently large time depending on $c$.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-04T07:00:00Z
DOI: 10.1137/20M1385056
Issue No: Vol. 60, No. 2 (2022)

• Geometrical Characterization of Sensor Placement for Cone-Invariant and
Multi-Agent Systems against Undetectable Zero-Dynamics Attacks

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Authors: Jianqi Chen, Jieqiang Wei, Wei Chen, Henrik Sandberg, Karl Henrik Johansson, Jie Chen
Pages: 890 - 916
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 890-916, April 2022.
Undetectable attacks are an important class of malicious attacks threatening the security of cyber-physical systems, which can modify a system's state but leave the system output measurements unaffected and hence cannot be detected from the output. This paper studies undetectable attacks on cone-invariant systems and multi-agent systems. We first provide a general characterization of zero-dynamics attacks, which characterizes fully undetectable attacks targeting the nonminimum phase zeros of a system. This geometrical characterization makes it possible to develop a defense strategy seeking to place a minimal number of sensors to detect and counter the zero-dynamics attacks on the system's actuators. The detect and defense scheme amounts to computing a set containing potentially vulnerable actuator locations and nodes and a defense union for feasible placement of sensors based on the geometrical properties of the cones under consideration.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-07T07:00:00Z
DOI: 10.1137/21M1403618
Issue No: Vol. 60, No. 2 (2022)

• Localized Stability Certificates for Spatially Distributed Systems over
Sparse Proximity Graphs

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Authors: Nader Motee, Qiyu Sun
Pages: 917 - 944
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 917-944, April 2022.
In this paper, we focus on localized conditions to verify exponential stability of spatially distributed linear systems whose interconnection structures are defined using a geodesic on proximity coupling graphs. We reformulate the exponential stability condition in the form of a feasibility condition that is amenable to localized implementations. Using finite truncation techniques, we obtain decentralized necessary and sufficient stability certificates. In order to guarantee global stability, it suffices to certify localized conditions over a graph covering, where the computational complexity of the verification of the proposed localized certificate is independent of network size. Several robustness conditions against local matrix perturbations are obtained that are useful for tuning network parameters in a decentralized manner while ensuring global exponential stability.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-11T07:00:00Z
DOI: 10.1137/19M1298834
Issue No: Vol. 60, No. 2 (2022)

• Stationary Markov Nash Equilibria for Nonzero-Sum Constrained ARAT Markov
Games

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Authors: François Dufour, Tomás Prieto-Rumeau
Pages: 945 - 967
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 945-967, April 2022.
We consider a nonzero-sum Markov game on an abstract measurable state space with compact metric action spaces. The goal of each player is to maximize his respective discounted payoff function under the condition that some constraints on a discounted payoff are satisfied. We are interested in the existence of a Nash or noncooperative equilibrium. Under suitable conditions, which include absolute continuity of the transitions with respect to some reference probability measure, additivity of the payoffs and the transition probabilities (ARAT condition), and continuity in action of the payoff functions and the density function of the transitions of the system, we establish the existence of a constrained stationary Markov Nash equilibrium, that is, the existence of stationary Markov strategies for each of the players yielding an optimal profile within the class of all history-dependent profiles.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-11T07:00:00Z
DOI: 10.1137/21M144565X
Issue No: Vol. 60, No. 2 (2022)

• On the Time-Inconsistent Deterministic Linear-Quadratic Control

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Authors: Hongyan Cai, Danhong Chen, Yunfei Peng, Wei Wei
Pages: 968 - 991
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 968-991, April 2022.
A fundamental theory of deterministic linear-quadratic (LQ) control is the equivalent relationship between control problems, two-point boundary value problems, and Riccati equations. In this paper, we extend the equivalence to a general time-inconsistent deterministic LQ problem, where the inconsistency arises from nonexponential discount functions. By studying the solvability of the Riccati equation, we show the existence and uniqueness of the linear equilibrium for the time-inconsistent LQ problem.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-18T07:00:00Z
DOI: 10.1137/21M1419611
Issue No: Vol. 60, No. 2 (2022)

• Event-Triggered Distributed Estimation with Decaying Communication Rate

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Authors: Xingkang He, Yu Xing, Junfeng Wu, Karl H. Johansson
Pages: 992 - 1017
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 992-1017, April 2022.
We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. Different sensors can have different step sizes and triggering thresholds, enabling the parameter estimation process to be conducted in a fully distributed way. We prove that the proposed algorithm has mean-square and almost-sure convergence, respectively, under an integrated condition of sensor network topology and sensor measurement matrices. The condition is satisfied if the topology is a balanced digraph containing a spanning tree and the system is collectively observable. The collective observability is the possibly mildest condition, since it is a spatially and temporally collective condition of all sensors and allows sensor measurement matrices to be time-varying, stochastic, and nonstationary. Moreover, we provide estimates for the convergence rates, which are related to the step size as well as the triggering threshold. Furthermore, as an essential metric of sensor communication intensity in the event-triggered distributed algorithms, the communication rate is proved to decay to zero with a certain speed almost surely as time goes to infinity. In addition, we show that it is feasible to tune the threshold and the step size such that requirements of algorithm convergence and communication rate decay are satisfied simultaneously. We also show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate. Specifically, increasing the decay speed of the threshold would make the communication rate decay faster but reduce the convergence rate of the estimation error. Numerical simulations are provided to illustrate the developed results.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-19T07:00:00Z
DOI: 10.1137/21M1405083
Issue No: Vol. 60, No. 2 (2022)

• Necessary Second-Order Conditions for a Local Infimum in an Optimal
Control

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Authors: E. R. Avakov, G. G. Magaril-Il'yaev
Pages: 1018 - 1038
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1018-1038, April 2022.
Necessary second-order conditions for a local infimum in an optimal control problem are proved (the concept of a local infimum generalizes that of an optimal trajectory). The results obtained strengthen the Pontryagin maximum principle and the known necessary second-order optimality conditions.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-21T07:00:00Z
DOI: 10.1137/21M1389973
Issue No: Vol. 60, No. 2 (2022)

• Optimal Ergodic Harvesting under Ambiguity

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Authors: Asaf Cohen, Alexandru Hening, Chuhao Sun
Pages: 1039 - 1063
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1039-1063, April 2022.
We consider an ergodic harvesting problem with model ambiguity that arises from biology. To account for the ambiguity, the problem is constructed as a stochastic game with two players: the decision maker (DM) chooses the “best” harvesting policy, and an adverse player chooses the “worst” probability measure. The main result is establishing an optimal strategy (also referred to as a control) of the DM and showing that it is a threshold policy. The optimal threshold and the optimal payoff are obtained by solving a free-boundary problem emerging from the Hamilton--Jacobi--Bellman (HJB) equation. As part of the proof, we fix a gap that appeared in the HJB analysis of [Alvarez and Hening, Stochastic Process. Appl., 2019, in press], a paper that analyzed the risk-neutral version of the ergodic harvesting problem. Finally, we study the dependence of the optimal threshold and the optimal payoff on the ambiguity parameter and show that if the ambiguity goes to 0, the problem converges to the risk-neutral problem.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-21T07:00:00Z
DOI: 10.1137/21M1413262
Issue No: Vol. 60, No. 2 (2022)

• An Enhanced Strategy for Adaptive Output-Feedback Control of Uncertain
Nonlinear Systems

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Authors: Yuan Wang, Yungang Liu
Pages: 1064 - 1091
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1064-1091, April 2022.
We are concerned with global practical tracking for uncertain nonlinear systems with intrinsic dependence on unmeasured states. Typically, we aim to remove the polynomial restrictions on system growth rates in relevant works, admitting the rates to be of arbitrary function-of-output type. Note that such highly nonlinear rates could alter so drastically in magnitude that no polynomial can upper bound them on any unbounded region, and would undermine the design rationale and the analysis route that are followed in polynomial rate scenarios. This entails refining/integrating state-of-the-art techniques and developing new ones, from design to analysis. Concretely, to circumvent the need for knowledge on polynomials, dual dynamic high gains are devised ingeniously which particularly incorporate certain highly nonlinear components to counteract the arbitrary function-of-output rates and other inherent nonlinearities and uncertainties. But unfavorable impacts of the components would also arise putting the effectiveness of the high gains at risk, which necessitates noticeable changes in the choice of vital variables/functions. In addition, to provide tractable observer error dynamics for control design and analysis, a nonlinear high-gain observer is pursued, which is filter based and of reduced order, following the design methodologies in relevant works. In terms of performance analysis, practical tracking suffers extra time variants and additive uncertainties which together with the highly nonlinear rates would carry over to stability analysis inevitably and in turn ruin the existing analysis routes. Hence, a new analysis route is paved to verify the anticipated performance. Notably, by unfolding an important implication, the performance verification boils down largely to the boundedness of the high gains. This makes plain and intelligible the verification, although sophisticated integration of multiple composite Lyapunov functions is still required. An exception, i.e., asymptotic stabilization, is presented to demonstrate its close connection with practical tracking.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-25T07:00:00Z
DOI: 10.1137/20M1389182
Issue No: Vol. 60, No. 2 (2022)

• The Structure of Optimal Protocols for a Mathematical Model of
Chemotherapy with Antiangiogenic Effects

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Authors: Urszula Ledzewicz, Heinz Schättler
Pages: 1092 - 1116
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1092-1116, April 2022.
We analyze a mathematical model for cancer chemotherapy which includes antiangiogenic effects of the cytotoxic agent. Assuming that the total amount of agents to be given has been determined a priori based on a medical assessment of its side effects, we consider the problem how to best administer this amount. The model assumes a homogenous tumor and if the aim is to minimize the tumor volume, then optimal controls administer the total dose in a single maximum dose session. As, however, angiogenic effects of the agent are taken into account, this no longer is optimal. Lower dose strategies determined by an optimal singular arc with significantly reduced dose rates give a better response over time. In this paper, for the medically realistic domain of the mathematical model, the concatenation structure of optimal controlled trajectories as segments of bang and singular arcs is determined. This leads to simple numerical minimization procedures for the computation of globally optimal controls.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-25T07:00:00Z
DOI: 10.1137/21M1395326
Issue No: Vol. 60, No. 2 (2022)

• Global Stabilization of Compressible Flow between Two Moving Pistons

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Authors: Iasson Karafyllis, Miroslav Krstic
Pages: 1117 - 1142
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1117-1142, April 2022.
This paper studies the global feedback stabilization problem of a system with two pistons and the area between them containing a viscous compressible fluid (gas) modeled by the Navier--Stokes equations. The control input is the force applied on the left piston (boundary input) and the overall system consists of two nonlinear partial differential equations and four nonlinear ordinary differential equations. Global feedback stabilizers are designed for the overall system by means of the control Lyapunov functional methodology. The closed-loop system exhibits global asymptotic stability with an exponential convergence rate. The proposed stabilizing boundary feedback laws do not require measurement of the density and velocity profiles inside the area between the pistons and simply require measurements of the gas density and velocity at the position of the actuated piston.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-27T07:00:00Z
DOI: 10.1137/21M1413869
Issue No: Vol. 60, No. 2 (2022)

• On Singularities of Minimum Time Control-Affine Systems

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Authors: Jean-Baptiste Caillau, Jacques Féjoz, Michaël Orieux, Robert Roussarie
Pages: 1143 - 1162
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1143-1162, April 2022.
Affine control problems arise naturally from controlled mechanical systems. Building on previous results [Agrachev and Biolo, J. Dyn. Control Syst., 23 (2017), pp. 577--595; Caillau and Daoud, SIAM J. Control Optim., 50 (2012), pp. 3178--3202], we prove that, in the case of time minimization with control on the disk, the extremal flow given by Pontrjagin's maximum principle is smooth along the strata of a well-chosen stratification. We also study this flow in terms of regular-singular transition and prove that the singularity along time-minimizing extremals crossing these strata is at most logarithmic. We then apply these results to mechanical systems, paying special attention to the case of the controlled three-body problem.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-27T07:00:00Z
DOI: 10.1137/20M1366861
Issue No: Vol. 60, No. 2 (2022)

• A Nash-Type Fictitious Game Framework to Time-Inconsistent Stochastic
Control Problems

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Authors: Yuan-Hua Ni, Binbin Si, Xinzhen Zhang
Pages: 1163 - 1189
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1163-1189, April 2022.
In this paper, a Nash-type fictitious game framework is introduced for handling time-inconsistent linear-quadratic (LQ) optimal control problems. The Nash-type game in this framework is called fictitious as it is between the decision maker (called real player) and an auxiliary control variable (called fictitious player) with the real player and fictitious player looking for time-consistent policy and precommitted optimal policy, respectively. Namely, the fictitious game framework is actually an auxiliary-variable-based mechanism where the fictitious player is our particular design. Noting that the real player's cost functional is revised in accordance with that of the fictitious player, the equilibrium policy of the real player is called an open-loop self-coordination control of the original LQ problem. As a generalization, a time-inconsistent nonzero-sum stochastic linear-quadratic dynamic game is investigated, where one player looks for a precommitted optimal policy and the other player searches for a time-consistent policy. Necessary and sufficient conditions are presented to ensure the existence of open-loop equilibrium of the nonzero-sum game, which resort to a set of Riccati-like equations and linear equations. By applying the developed theory of nonzero-sum game, open-loop self-coordination control of the LQ optimal control is fully characterized, and multiperiod mean-variance portfolio selection is also investigated.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-27T07:00:00Z
DOI: 10.1137/19M1281885
Issue No: Vol. 60, No. 2 (2022)

• ERRATUM: LP Formulations of Discrete Time Long-Run Average Optimal Control
Problems: The Nonergodic Case

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Authors: Vivek S. Borkar, Vladimir Gaitsgory, Ilya Shvartsman
Pages: 1190 - 1192
Abstract: SIAM Journal on Control and Optimization, Volume 60, Issue 2, Page 1190-1192, April 2022.
One of the proofs in our paper [SIAM J. Control Optim., 57 (2019), pp. 1783--1817] contains an error. This erratum addresses the issue.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-28T07:00:00Z
DOI: 10.1137/21M1466359
Issue No: Vol. 60, No. 2 (2022)

• Revisiting SIR in the Age of COVID-19: Explicit Solutions and Control
Problems

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Authors: Vivek S. Borkar, D. Manjunath
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
The nonpopulation conserving SIR (SIR-NC) model to describe the spread of infections in a community is studied. Unlike the standard SIR model, this does not assume population conservation. Although similar in form to the standard SIR, SIR-NC admits a closed form solution while allowing us to model mortality and also provides a different, and arguably a more realistic, interpretation of model parameters. Numerical comparisons of this SIR-NC model with the standard, population conserving, SIR model are provided. Extensions to include imported infections, interacting communities, and models that include births and deaths are presented and analyzed. Several numerical examples are also presented to illustrate these models. A discrete time control problem for the SIR-NC epidemic model is presented in which the cost function depends on variables that correspond to the levels of lockdown, the level of testing and quarantine, and the number of infections. We include a switching cost for moving between lockdown levels. Numerical experiments are presented.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-11T07:00:00Z
DOI: 10.1137/20M1372913

• Optimal Incentives to Mitigate Epidemics: A Stackelberg Mean Field Game
Approach

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Authors: Alexander Aurell, René Carmona, Gökçe Dayanikli, Mathieu Laurière
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
Motivated by the models of epidemic control in large populations, we consider a Stackelberg mean field game model between a principal and a mean field of agents whose states evolve in a finite state space. The agents play a noncooperative game in which they control their rates of transition between states to minimize an individual cost. The principal influences the nature of the resulting Nash equilibrium through incentives to optimize its own objective. We analyze this game using a probabilistic approach. We then propose an application to an epidemic model of SIR type in which the agents control the intensities of their interactions, and the principal is a regulator acting with nonpharmaceutical interventions. To compute the solutions, we propose an innovative numerical approach based on Monte Carlo simulations and machine learning tools for stochastic optimization. We conclude with numerical experiments illustrating the impact of the agents' and the regulator's optimal decisions in two specific models: a basic SIR model with semiexplicit solutions and a more complex model with a larger state space.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-07T07:00:00Z
DOI: 10.1137/20M1377862

• Convergence and Equilibria Analysis of a Networked Bivirus Epidemic Model

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Authors: Mengbin Ye, Brian D. O. Anderson, Ji Liu
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
This paper studies a networked bivirus model, in which two competing viruses spread across a network of interconnected populations; each node represents a population with a large number of individuals. The viruses may spread through possibly different network structures, and an individual cannot be simultaneously infected with both viruses. Focusing on convergence and equilibria analysis, a number of new results are provided. First, we show that for networks with generic system parameters, there exist a finite number of equilibria. Exploiting monotone systems theory, we further prove that for bivirus networks with generic system parameters, convergence to an equilibrium occurs for all initial conditions, except possibly for a set of measure zero. Given the network structure of one virus, a method is presented to construct an infinite family of network structures for the other virus that results in an infinite number of equilibria in which both viruses coexist. Necessary and sufficient conditions are derived for the local stability/instability of boundary equilibria, in which one virus is present and the other is extinct. A sufficient condition for a boundary equilibrium to be almost globally stable is presented. Then, we show how to use monotone systems theory to generate conclusions on the ordering of stable and unstable equilibria, and in some instances identify the number of equilibria via rapid simulation testing. Last, we provide an analytical method for computing equilibria in networks with only two nodes, and show that it is possible for a bivirus network to have an unstable coexistence equilibrium and two locally stable boundary equilibria.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-07T07:00:00Z
DOI: 10.1137/20M1369014

• A Finite Memory Interacting Pólya Contagion Network and Its
Approximating Dynamical Systems

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Authors: Somya Singh, Fady Alajaji, Bahman Gharesifard
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
We introduce a new model for contagion spread using a network of interacting finite memory two-color Pólya urns, which we refer to as the finite memory interacting Pólya contagion network. The urns interact in the sense that the probability of drawing a red ball (which represents an infection state) for a given urn, not only depends on the ratio of red balls in that urn but also on the ratio of red balls in the other urns in the network, hence accounting for the effect of spatial contagion. The resulting networkwide contagion process is a discrete-time finite-memory ($M$th order) Markov process, whose transition probability matrix is determined. The stochastic properties of the network contagion Markov process are analytically examined, and for homogeneous system parameters, we characterize the limiting state of infection in each urn. For the nonhomogeneous case, given the complexity of the stochastic process, and in the same spirit as the well-studied SIS models, we use a mean-field type approximation to obtain a discrete-time dynamical system for the finite memory interacting Pólya contagion network. Interestingly, for $M=1$, we obtain a linear dynamical system which exactly represents the corresponding Markov process. For $M>1$, we use mean-field approximation to obtain a nonlinear dynamical system. Furthermore, noting that the latter dynamical system admits a linear variant (realized by retaining its leading linear terms), we study the asymptotic behavior of the linear systems for both memory modes and characterize their equilibrium. Finally, we present simulation studies to assess the quality of the approximation purveyed by the linear and nonlinear dynamical systems.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-07T07:00:00Z
DOI: 10.1137/20M1370525

• Adaptive Test Allocation for Outbreak Detection and Tracking in Social
Contact Networks

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Authors: Pau Batlle, Joan Bruna, Carlos Fernandez-Granda, Victor M. Preciado
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
We present a general framework for adaptive allocation of viral tests in social contact networks and arbitrary epidemic models. We pose and solve several complementary problems. First, we consider the design of a social sensing system whose objective is the early detection of a novel epidemic outbreak. In particular, we propose an algorithm to select a subset of individuals to be tested in order to detect the onset of an epidemic outbreak as fast as possible. We pose this problem as a hitting time probability maximization problem and use submodularity optimization and Monte Carlo techniques to obtain solutions with explicit quality guarantees. Second, once an epidemic outbreak has been detected, we consider the problem of using the data from the sensing system to obtain estimates of the initial patient and the current status of the epidemic. Finally, we consider the problem of adaptively distributing viral tests over time in order to maximize the information gained about the current state of the epidemic. We formalize this problem in terms of mutual information and propose an adaptive allocation strategy with quality guarantees. For these problems, we derive analytical solutions for any stochastic compartmental epidemic model with Markovian dynamics, as well as efficient Monte Carlo--based algorithms for non-Markovian dynamics or large networks. We illustrate the performance of the proposed framework in numerical experiments involving a model of COVID-19 applied to a real human contact network.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-04-04T07:00:00Z
DOI: 10.1137/20M1377874

• Edge Deletion Algorithms for Minimizing Spread in SIR Epidemic Models

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Authors: Yuhao Yi, Liren Shan, Philip E. Paré, Karl Henrik Johansson
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
This paper studies algorithmic strategies to effectively reduce the number of infections in susceptible-infected-recovered (SIR) epidemic models. We consider a Markov chain SIR model and its two instantiations in the deterministic SIR (D-SIR) model and the independent cascade SIR (IC-SIR) model. We investigate the problem of minimizing the number of infections by restricting contacts under realistic constraints. Under moderate assumptions on the reproduction number, we prove that the infection numbers are bounded by supermodular functions in the D-SIR model and the IC-SIR model for large classes of random networks. We propose efficient algorithms with approximation guarantees to minimize infections. The theoretical results are illustrated by numerical simulations.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-31T07:00:00Z
DOI: 10.1137/20M1377011

• A Distributed Optimal Control Model Applied to COVID-19 Pandemic

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Authors: Raimund M. Kovacevic, Nikolaos I. Stilianakis, Vladimir M. Veliov
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
In this paper, a distributed optimal control epidemiological model is presented. The model describes the dynamics of an epidemic with social distancing as a control policy. The model belongs to the class of continuous-time models, usually involving ordinary/partial differential equations, but has a novel feature. The core model---a single integral equation---does not explicitly use transition rates between compartments. Instead, it is based on statistical information on the disease status of infected individuals, depending on the time since infection. The approach is especially relevant for the coronavirus disease 2019 (COVID-19) in which infected individuals are infectious before onset of symptoms during a relatively long incubation period. Based on the analysis of the proposed optimal control problem, including necessary optimality conditions, this paper outlines some efficient numerical approaches. Numerical solutions show some interesting features of the optimal policy for social distancing, depending on the weights attributed to the number of isolated individuals with symptoms and to economic losses due to the enforcement of the control policy. The general nature of the model allows for inclusion of additional epidemic features with minor adaptations in the basic equations. Therefore, the modeling approach may contribute to the analysis of combined intervention strategies and to the guidance of public health decisions.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-24T07:00:00Z
DOI: 10.1137/20M1373840

• Respect the Unstable: Delays and Saturation in Contact Tracing for Disease
Control

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Authors: Richard Pates, Andres Ferragut, Elijah Pivo, Pengcheng You, Fernando Paganini, Enrique Mallada
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
Motivated by the novel coronavirus disease (COVID-19) pandemic, this paper aims to apply Gunter Stein's cautionary message of respecting the unstable to the problem of controlling the spread of an infectious disease. With this goal, we study the effect that delays and capacity constraints have in the test, trace, and isolate (TeTrIs) process, and how they impact its ability to prevent exponential disease spread. Our analysis highlights the critical importance of speed and scale in the TeTrIs process. Precisely, ensuring that the delay in the TeTrIs process is much smaller than the doubling time of the disease spread is necessary for achieving acceptable performance. Similarly, limited TeTrIs capacity introduces a threshold on the size of an outbreak beyond which the disease spreads almost like the uncontrolled case. Along the way, we provide numerical illustrations to highlight these points.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-03-09T08:00:00Z
DOI: 10.1137/20M1377825

• Initialization and Curing Policies for Pólya Contagion Networks

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Authors: Greg Harrington, Fady Alajaji, Bahman Gharesifard
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
We investigate optimization policies for resource distribution in network epidemics using a model that derives from the classical Pólya process. This model, called the Pólya network contagion process, is based on a modified urn sampling scheme that accounts for both temporal and spatial contagion between neighboring nodes in a network. We study two infection mitigation problems---one which takes place upon initialization and one which occurs continually as the Pólya network process develops. We frame these problems as resource allocation problems with fixed budgets and analyze a suite of potential policies. Due to the complexity of these problems, we introduce effective proxy measures for the average infection rate in each case. We also prove that the two-sided infection-curing game on the so-called expected network exposure admits a Nash equilibrium. In both the curing and initialization scenarios, we introduce heuristic policies that primarily function on the basis of limiting the number of targeted nodes within a particular network setup. Simulations are run for mid-to-large--scale networks to compare performance of our heuristics to provably convergent gradient descent algorithms run on the simplified proxy measures.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-01-24T08:00:00Z
DOI: 10.1137/20M1358803

• How Much Testing and Social Distancing is Required to Control
COVID-19' Some Insight Based on an Age-Differentiated Compartmental
Model

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Authors: Sara Grundel, Stefan Heyder, Thomas Hotz, Tobias K. S. Ritschel, Philipp Sauerteig, Karl Worthmann
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
In this paper, we provide insights on how much testing and social distancing is required to control COVID-19. To this end, we develop a compartmental model that accounts for key aspects of the disease: incubation time, age-dependent symptom severity, and testing and hospitalization delays; the model's parameters are chosen based on medical evidence, and, for concreteness, adapted to the German situation. Then, optimal mass-testing and age-dependent social distancing policies are determined by solving optimal control problems both in open loop and within a model predictive control framework. We aim to minimize testing and/or social distancing until herd immunity sets in under a constraint on the number of available intensive care units. We find that an early and short lockdown is inevitable but can be slowly relaxed over the following months.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-01-13T08:00:00Z
DOI: 10.1137/20M1377783

• The Role of Asymptomatic Infections in the COVID-19 Epidemic via Complex
Networks and Stability Analysis

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Authors: Leonardo Stella, Alejandro Pinel Martínez, Dario Bauso, Patrizio Colaneri
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
Italy was the first country to be affected by the COVID-19 epidemic in Europe. In the past months, predictive mathematical models have been used to understand the proportion of this epidemic and identify effective policies to control it, but few have considered the impact of asymptomatic or paucisymptomatic infections in a structured setting. A critical problem that hinders the accuracy of these models is indeed given by the presence of a large number of asymptomatic individuals in the population. This number is estimated to be large, sometimes between 3 and 10 times the diagnosed patients. We focus on this aspect through the formulation of a model that captures two types of interactions---one with asymptomatic individuals and another with symptomatic infected. We also extend the original model to capture the interactions in the population via complex networks, and, in particular, the Watts--Strogatz model, which is the most suitable for social networks. The contributions of this paper include (i) the formulation of an epidemic model, which we call SAIR, that discriminates between asymptomatic and symptomatic infected through different measures of interactions and the corresponding stability analysis of the system in feedback form through the calculation of the $\mathcal R_0$ as $H_\infty$ gain; (ii) the analysis of the corresponding structured model involving the Watts and Strogatz interaction topology, to study the case of heterogeneous connectivity in the population; (iii) a case study on the Italian case, where we take into account the Istat seroprevalence study in the homogeneous case first, and then we analyze the impact of summer tourism and of the start of school in September in the heterogeneous case.
Citation: SIAM Journal on Control and Optimization
PubDate: 2022-01-11T08:00:00Z
DOI: 10.1137/20M1373335

• Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical
Networks

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Authors: Ashish R. Hota, Tanya Sneh, Kavish Gupta
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
We investigate the evolution of epidemics over dynamical networks when nodes choose to interact with others in a selfish and decentralized manner. Specifically, we analyze the susceptible-asymptomatic-infected-recovered (SAIR) epidemic in the framework of activity-driven networks with heterogeneous node degrees and time-varying activation rates and derive both individual- and degree-based mean-field approximations of the exact state evolution. We then present a game-theoretic model where nodes choose their activation probabilities in a strategic manner using current state information as feedback, and we characterize the quantal response equilibrium (QRE) of the proposed setting. We then consider the activity-driven susceptible-infected-susceptible (SIS) epidemic model, characterize equilibrium activation probabilities, and analyze epidemic evolution in closed-loop. Our numerical results provide compelling insights into epidemic evolution under game-theoretic activation. Specifically, for the SAIR epidemic, we show that under suitable conditions, the epidemic can persist, as any decrease in infected proportion is counteracted by an increase in activity rates by the nodes. For the SIS epidemic, we show that in regimes where there is an endemic state, the infected proportion could be significantly smaller under game-theoretic activation if the loss upon infection is sufficiently high.
Citation: SIAM Journal on Control and Optimization
PubDate: 2021-12-14T08:00:00Z
DOI: 10.1137/20M1376923

• Parameter Estimation in Epidemic Spread Networks Using Limited
Measurements

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Authors: Lintao Ye, Philip E. Paré, Shreyas Sundaram
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
We study the problem of estimating the parameters (i.e., infection rate and recovery rate) governing the spread of epidemics in networks. Such parameters are typically estimated by measuring various characteristics (such as the number of infected and recovered individuals) of the infected populations over time. However, these measurements also incur certain costs, depending on the population being tested and the times at which the tests are administered. We thus formulate the epidemic parameter estimation problem as an optimization problem, where the goal is to either minimize the total cost spent on collecting measurements or to optimize the parameter estimates while remaining within a measurement budget. We show that these problems are NP-hard to solve in general and then propose approximation algorithms with performance guarantees. We validate our algorithms using numerical examples.
Citation: SIAM Journal on Control and Optimization
PubDate: 2021-11-30T08:00:00Z
DOI: 10.1137/20M1377801

• Optimal Switching between Locking Down and Opening the Economy Because of
an Infection

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Authors: Maxim Bichuch
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
We consider a two-regime switching model with the goal of minimizing the expected discounted cumulative combination of the number of infections together with an inverse economical indicator. We assume the two regime choices are between opening and locking down the economy, and the choice affects the infection rate. We also assume that the economy level also has a small influence on both the infection rate and the cumulative function being minimized. We then asymptotically find the value function and the boundaries of the stopping regions and perform a numerical calibration to draw conclusions about optimal lockdown in a pandemic.
Citation: SIAM Journal on Control and Optimization
PubDate: 2021-11-30T08:00:00Z
DOI: 10.1137/20M1377631

• Identifiability of Infection Model Parameters Early in an Epidemic

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Authors: Timothy Sauer, Tyrus Berry, Donald Ebeigbe, Michael M. Norton, Andrew J. Whalen, Steven J. Schiff
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
It is known that the parameters in the deterministic and stochastic SEIR epidemic models are structurally identifiable. For example, from knowledge of the infected population time series $I(t)$ during the entire epidemic, the parameters can be successfully estimated. In this article we observe that estimation will fail in practice if only infected case data during the early part of the epidemic (prepeak) is available. This fact can be explained using a well-known phenomenon called dynamical compensation. We use this concept to derive an unidentifiability manifold in the parameter space of SEIR that consists of parameters indistinguishable from $I(t)$ early in the epidemic. Thus, identifiability depends on the extent of the system trajectory that is available for observation. Although the existence of the unidentifiability manifold obstructs the ability to exactly determine the parameters, we suggest that it may be useful for uncertainty quantification purposes. A variant of SEIR recently proposed for COVID-19 modeling is also analyzed, and an analogous unidentifiability surface is derived.
Citation: SIAM Journal on Control and Optimization
PubDate: 2021-11-01T07:00:00Z
DOI: 10.1137/20M1353289

• Optimal Control of Diseases in Prison Populations through Screening
Policies of New Inmates

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Authors: Pedro Gajardo, Victor Riquelme, Diego Vicencio
Abstract: SIAM Journal on Control and Optimization, Ahead of Print.
In this paper, we study an optimal control problem of a communicable disease in a prison population. In order to control the spread of the disease inside a prison, we consider an active case-finding strategy, consisting of screening a proportion of new inmates at the entry point, followed by a treatment depending on the results of this procedure. The control variable consists then in the coverage of the screening applied to new inmates. The disease dynamics is modeled by a SIS (susceptible-infected-susceptible) model, typically used to represent diseases that do not confer immunity after infection. We determine the optimal strategy that minimizes a combination between the cost of the screening/treatment at the entrance and the cost of maintaining infected individuals inside the prison, in a given time horizon. Using the Pontryagin maximum principle and the Hamilton--Jacobi--Bellman equation, among other tools, we provide the complete synthesis of an optimal feedback control, consisting in a bang-bang strategy with at most two switching times and no singular arc trajectory, characterizing different profiles depending on model parameters.
Citation: SIAM Journal on Control and Optimization
PubDate: 2021-08-24T07:00:00Z
DOI: 10.1137/20M1378582

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