Subjects -> MATHEMATICS (Total: 1028 journals)
    - APPLIED MATHEMATICS (92 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (729 journals)
    - MATHEMATICS (GENERAL) (45 journals)
    - NUMERICAL ANALYSIS (26 journals)
    - PROBABILITIES AND MATH STATISTICS (113 journals)

MATHEMATICS (729 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 1)
Accounting Perspectives     Full-text available via subscription   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 13)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 5)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 38)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 2)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 5)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 8)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 5)
Advances in Catalysis     Full-text available via subscription   (Followers: 7)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 20)
Advances in Decision Sciences     Open Access   (Followers: 4)
Advances in Difference Equations     Open Access   (Followers: 2)
Advances in Fixed Point Theory     Open Access  
Advances in Geosciences (ADGEO)     Open Access   (Followers: 19)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 9)
Advances in Materials Science     Open Access   (Followers: 19)
Advances in Mathematical Physics     Open Access   (Followers: 5)
Advances in Mathematics     Full-text available via subscription   (Followers: 18)
Advances in Numerical Analysis     Open Access   (Followers: 4)
Advances in Operations Research     Open Access   (Followers: 13)
Advances in Operator Theory     Hybrid Journal  
Advances in Pure Mathematics     Open Access   (Followers: 8)
Advances in Science and Research (ASR)     Open Access   (Followers: 8)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 7)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 5)
Afrika Matematika     Hybrid Journal   (Followers: 2)
Air, Soil & Water Research     Open Access   (Followers: 6)
AKSIOMATIK : Jurnal Penelitian Pendidikan dan Pembelajaran Matematika     Open Access  
Al-Jabar : Jurnal Pendidikan Matematika     Open Access  
Al-Qadisiyah Journal for Computer Science and Mathematics     Open Access   (Followers: 3)
AL-Rafidain Journal of Computer Sciences and Mathematics     Open Access   (Followers: 3)
Algebra and Logic     Hybrid Journal   (Followers: 7)
Algebra Colloquium     Hybrid Journal   (Followers: 1)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Open Access   (Followers: 5)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 8)
American Journal of Mathematical Analysis     Open Access   (Followers: 1)
American Journal of Mathematical and Management Sciences     Hybrid Journal  
American Journal of Mathematics     Full-text available via subscription   (Followers: 8)
American Journal of Operations Research     Open Access   (Followers: 7)
American Mathematical Monthly     Full-text available via subscription   (Followers: 3)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 12)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access  
Analysis and Applications     Hybrid Journal   (Followers: 2)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 9)
Anargya : Jurnal Ilmiah Pendidikan Matematika     Open Access  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 3)
Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 14)
Annals of Functional Analysis     Hybrid Journal   (Followers: 2)
Annals of Mathematics     Full-text available via subscription   (Followers: 3)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 16)
Annals of PDE     Hybrid Journal  
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access   (Followers: 1)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 1)
Annuaire du Collège de France     Open Access   (Followers: 6)
ANZIAM Journal     Open Access   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applications of Mathematics     Hybrid Journal   (Followers: 2)
Applied Categorical Structures     Hybrid Journal   (Followers: 3)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Mathematics     Open Access   (Followers: 7)
Applied Mathematics     Open Access   (Followers: 6)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 10)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal   (Followers: 1)
Applied Mathematics and Nonlinear Sciences     Open Access  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 1)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 2)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 4)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 3)
Arabian Journal of Mathematics     Open Access   (Followers: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Arkiv för Matematik     Hybrid Journal  
Armenian Journal of Mathematics     Open Access  
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites     Open Access   (Followers: 19)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Research Journal of Mathematics     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 4)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 2)
Banach Journal of Mathematical Analysis     Hybrid Journal  
Basin Research     Hybrid Journal   (Followers: 6)
BIBECHANA     Open Access  
Biomath     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription  
British Journal for the History of Mathematics     Hybrid Journal  
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 17)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 1)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 3)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 2)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of Symbolic Logic     Full-text available via subscription   (Followers: 3)
Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics     Open Access  
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 2)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the Iranian Mathematical Society     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 2)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Cadernos do IME : Série Matemática     Open Access  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Mathematics / Journal canadien de mathématiques     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 20)
Canadian Mathematical Bulletin     Hybrid Journal  
Carpathian Mathematical Publications     Open Access  
Catalysis in Industry     Hybrid Journal  
CEAS Space Journal     Hybrid Journal   (Followers: 6)
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 1)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Ciencia     Open Access  
CODEE Journal     Open Access  
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 2)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Hybrid Journal   (Followers: 3)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 18)
Commentarii Mathematici Helvetici     Hybrid Journal  
Communications in Advanced Mathematical Sciences     Open Access  
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 2)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 5)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Compositio Mathematica     Full-text available via subscription  
Comptes Rendus : Mathematique     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 3)
Computational and Mathematical Methods     Hybrid Journal  
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 1)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 11)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 9)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 8)
Confluentes Mathematici     Hybrid Journal  
Constructive Mathematical Analysis     Open Access  
Contributions to Discrete Mathematics     Open Access  
Contributions to Game Theory and Management     Open Access  
COSMOS     Hybrid Journal   (Followers: 1)
Cross Section     Full-text available via subscription   (Followers: 1)
Cryptography and Communications     Hybrid Journal   (Followers: 10)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 8)
Czechoslovak Mathematical Journal     Hybrid Journal  
Daya Matematis : Jurnal Inovasi Pendidikan Matematika     Open Access  
Demographic Research     Open Access   (Followers: 14)
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 33)
Desimal : Jurnal Matematika     Open Access  
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 2)
Differentsial'nye Uravneniya     Open Access  
Digital Experiences in Mathematics Education     Hybrid Journal   (Followers: 2)
Discrete Mathematics     Hybrid Journal   (Followers: 8)
Discrete Mathematics & Theoretical Computer Science     Open Access   (Followers: 1)
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae - General Algebra and Applications     Open Access  
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Diskretnaya Matematika     Full-text available via subscription  
Doklady Akademii Nauk     Open Access  
Doklady Mathematics     Hybrid Journal  
Eco Matemático     Open Access  

        1 2 3 4 | Last

Similar Journals
Journal Cover
Analysis and Applications
Journal Prestige (SJR): 1.188
Citation Impact (citeScore): 2
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0219-5305 - ISSN (Online) 1793-6861
Published by World Scientific Homepage  [120 journals]
  • Well-posedness and asymptotic behavior of an aggregation model with
           intrinsic interactions on sphere and other manifolds

    • Free pre-print version: Loading...

      Authors: Razvan C. Fetecau, Hansol Park, Francesco S. Patacchini
      Pages: 965 - 1017
      Abstract: Analysis and Applications, Volume 19, Issue 06, Page 965-1017, November 2021.
      We investigate a model for collective behavior with intrinsic interactions on Riemannian manifolds. We establish the well-posedness of measure-valued solutions (defined via mass transport) on sphere, as well as investigate the mean-field particle approximation. We study the long-time behavior of solutions to the model on sphere, where the primary goal is to establish sufficient conditions for a consensus state to form asymptotically. Well-posedness of solutions and the formation of consensus are also investigated for other manifolds (e.g., a hypercylinder).
      Citation: Analysis and Applications
      PubDate: 2021-04-19T07:00:00Z
      DOI: 10.1142/S0219530521500081
      Issue No: Vol. 19, No. 06 (2021)
       
  • A new sufficient condition for sparse vector recovery via [math] local
           minimization

    • Free pre-print version: Loading...

      Authors: Ning Bi, Jun Tan, Wai-Shing Tang
      Pages: 1019 - 1031
      Abstract: Analysis and Applications, Volume 19, Issue 06, Page 1019-1031, November 2021.
      In this paper, we provide a necessary condition and a sufficient condition such that any [math]-sparse vector [math] can be recovered from [math] via [math] local minimization. Moreover, we further verify that the sufficient condition is naturally valid when the restricted isometry constant of the measurement matrix [math] satisfies [math]. Compared with the existing [math] local recoverability condition [math], this result shows that [math] local recoverability contains more measurement matrices.
      Citation: Analysis and Applications
      PubDate: 2021-04-10T07:00:00Z
      DOI: 10.1142/S0219530521500068
      Issue No: Vol. 19, No. 06 (2021)
       
  • Robust wavelet-based estimation for varying coefficient dynamic models
           under long-dependent structures

    • Free pre-print version: Loading...

      Authors: Xingcai Zhou, Shaogao Lv
      Pages: 1033 - 1057
      Abstract: Analysis and Applications, Volume 19, Issue 06, Page 1033-1057, November 2021.
      This paper considers a class of robust estimation problems for varying coefficient dynamic models via wavelet techniques, which can adapt to local features of the underlying functions and has less restriction to the smoothness of the functions. The convergence rates and asymptotic distributions of the robust wavelet-based estimator are established when the design variables are stationary short-range dependent (SRD) and the errors are long-range dependent (LRD). Particularly, a rate of convergence [math] in terms of estimation consistency can be achievable when the true components satisfy certain smoothness for a LRD process. Furthermore, an asymptotic property of the proposed estimator is given to indicate the confidence level of our proposed method for varying coefficient models with LRD.
      Citation: Analysis and Applications
      PubDate: 2021-03-06T08:00:00Z
      DOI: 10.1142/S0219530521500032
      Issue No: Vol. 19, No. 06 (2021)
       
  • Probabilistic solutions to DAEs learning from physical data

    • Free pre-print version: Loading...

      Authors: Zongmin Wu, Ran Zhang
      Pages: 1093 - 1111
      Abstract: Analysis and Applications, Volume 19, Issue 06, Page 1093-1111, November 2021.
      The nonlinear chaotic differential/algebraic equation (DAE) has been established to simulate the nonuniform oscillations of the motion of a falling sphere in the non-Newtonian fluid. The DAE is obtained only by learning the experimental data with sparse optimization method. However, the deterministic solution will become increasingly inaccurate for long time approximation of the continuous system. In this paper, we introduce two probabilistic solutions to compute the totally DAE, the Random branch selection iteration (RBSI) and Random switching iteration (RSI). The samples are also taken as the reference trajectory to learn random parameter. The proposed probabilistic solutions can be regarded as the discrete analogues of differential inclusion and switching DAEs, respectively. They have been also compared with the deterministic method, i.e. backward differentiation formula (BDF). The deterministic methods only give limited candidates of all the probability solutions, while the RSI can include all the possible trajectories. The numerical results and statistical information criterion show that RSI can successfully reveal the sustaining instabilities of the motion itself and long time chaotic behavior.
      Citation: Analysis and Applications
      PubDate: 2021-05-31T07:00:00Z
      DOI: 10.1142/S021953052150010X
      Issue No: Vol. 19, No. 06 (2021)
       
  • Author index Volume 19 (2021)

    • Free pre-print version: Loading...

      Pages: 1113 - 1116
      Abstract: Analysis and Applications, Volume 19, Issue 06, Page 1113-1116, November 2021.

      Citation: Analysis and Applications
      PubDate: 2021-10-20T07:00:00Z
      DOI: 10.1142/S0219530521990013
      Issue No: Vol. 19, No. 06 (2021)
       
  • Effects of depth, width, and initialization: A convergence analysis of
           layer-wise training for deep linear neural networks

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      Authors: Yeonjong Shin
      Pages: 1 - 47
      Abstract: Analysis and Applications, Ahead of Print.
      Deep neural networks have been used in various machine learning applications and achieved tremendous empirical successes. However, training deep neural networks is a challenging task. Many alternatives have been proposed in place of end-to-end back-propagation. Layer-wise training is one of them, which trains a single layer at a time, rather than trains the whole layers simultaneously. In this paper, we study a layer-wise training using a block coordinate gradient descent (BCGD) for deep linear networks. We establish a general convergence analysis of BCGD and found the optimal learning rate, which results in the fastest decrease in the loss. We identify the effects of depth, width, and initialization. When the orthogonal-like initialization is employed, we show that the width of intermediate layers plays no role in gradient-based training beyond a certain threshold. Besides, we found that the use of deep networks could drastically accelerate convergence when it is compared to those of a depth 1 network, even when the computational cost is considered. Numerical examples are provided to justify our theoretical findings and demonstrate the performance of layer-wise training by BCGD.
      Citation: Analysis and Applications
      PubDate: 2021-12-31T08:00:00Z
      DOI: 10.1142/S0219530521500263
       
  • Weighted random sampling and reconstruction in general multivariate
           trigonometric polynomial spaces

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      Authors: Wei Li, Jun Xian
      Pages: 1 - 20
      Abstract: Analysis and Applications, Ahead of Print.
      The set of sampling and reconstruction in trigonometric polynomial spaces will play an important role in signal processing. However, in many applications, the frequencies in trigonometric polynomial spaces are not all integers. In this paper, we consider the problem of weighted random sampling and reconstruction of functions in general multivariate trigonometric polynomial spaces. The sampling set is randomly selected on a bounded cube with a probability distribution. We obtain that with overwhelming probability, the sampling inequality holds and the explicit reconstruction formula succeeds for all functions in the general multivariate trigonometric polynomial spaces when the sampling size is sufficiently large.
      Citation: Analysis and Applications
      PubDate: 2021-12-31T08:00:00Z
      DOI: 10.1142/S0219530521500330
       
  • Approximation, characterization, and continuity of multivariate monotonic
           regression functions

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      Authors: Jochen Schmid
      Pages: 1 - 39
      Abstract: Analysis and Applications, Ahead of Print.
      We deal with monotonic regression of multivariate functions [math] on a compact rectangular domain [math] in [math], where monotonicity is understood in a generalized sense: as isotonicity in some coordinate directions and antitonicity in some other coordinate directions. As usual, the monotonic regression of a given function [math] is the monotonic function [math] that has the smallest (weighted) mean-squared distance from [math]. We establish a simple general approach to compute monotonic regression functions: namely, we show that the monotonic regression [math] of a given function [math] can be approximated arbitrarily well — with simple bounds on the approximation error in both the [math]-norm and the [math]-norm — by the monotonic regression [math] of grid-constant functions [math]. monotonic regression algorithms. We also establish the continuity of the monotonic regression [math] of a continuous function [math] along with an explicit averaging formula for [math]. And finally, we deal with generalized monotonic regression where the mean-squared distance from standard monotonic regression is replaced by more complex distance measures which arise, for instance, in maximum smoothed likelihood estimation. We will see that the solution of such generalized monotonic regression problems is simply given by the standard monotonic regression [math].
      Citation: Analysis and Applications
      PubDate: 2021-12-23T08:00:00Z
      DOI: 10.1142/S0219530521500299
       
  • Global mass-preserving solutions to a chemotaxis-fluid model involving
           Dirichlet boundary conditions for the signal

    • Free pre-print version: Loading...

      Authors: Yulan Wang, Michael Winkler, Zhaoyin Xiang
      Pages: 1 - 30
      Abstract: Analysis and Applications, Ahead of Print.
      The chemotaxis-Stokes system nt + u ⋅∇n = ∇⋅ (nm−1∇n) −∇⋅ (n∇c),ct + u ⋅∇c = Δc − nc,ut = Δu + ∇P + n∇ϕ,∇⋅ u = 0 is considered subject to the boundary condition (nm−1∇n − n∇c) ⋅ ν = 0,c = c ∗(x,t),u = 0,x ∈ ∂Ω,t> 0, with [math] and a given nonnegative function [math]. In contrast to the well-studied case when the second requirement herein is replaced by a homogeneous Neumann boundary condition for [math], the Dirichlet condition imposed here seems to destroy a natural energy-like property that has formed a core ingredient in the literature by providing comprehensive regularity features of the latter problem. This paper attempts to suitably cope with accordingly poor regularity information in order to nevertheless derive a statement on global existence within a generalized framework of solvability which involves appropriately mild requirements on regularity, but which maintains mass conservation in the first component as a key solution property.
      Citation: Analysis and Applications
      PubDate: 2021-12-15T08:00:00Z
      DOI: 10.1142/S0219530521500275
       
  • A new elliptic mixed boundary value problem with [math]-Laplacian and
           Clarke subdifferential: Existence, comparison and convergence results

    • Free pre-print version: Loading...

      Authors: Shengda Zeng, Stanisław Migórski, Domingo A. Tarzia
      Pages: 1 - 20
      Abstract: Analysis and Applications, Ahead of Print.
      The goal of this paper is to investigate a new class of elliptic mixed boundary value problems involving a nonlinear and nonhomogeneous partial differential operator [math]-Laplacian, and a multivalued term represented by Clarke’s generalized gradient. First, we apply a surjectivity result for multivalued pseudomonotone operators to examine the existence of weak solutions under mild hypotheses. Then, a comparison theorem is delivered, and a convergence result, which reveals the asymptotic behavior of solution when the parameter (heat transfer coefficient) tends to infinity, is obtained. Finally, we establish a continuous dependence result of solution to the boundary value problem on the data.
      Citation: Analysis and Applications
      PubDate: 2021-12-09T08:00:00Z
      DOI: 10.1142/S0219530521500287
       
  • Boundedness and compactness of commutators associated with Lipschitz
           functions

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      Authors: Weichao Guo, Jianxun He, Huoxiong Wu, Dongyong Yang
      Pages: 1 - 37
      Abstract: Analysis and Applications, Ahead of Print.
      Let [math], [math] and [math] be a singular or fractional integral operator with homogeneous kernel [math]. In this paper, a CMO type space [math] is introduced and studied. In particular, the relationship between [math] and the Lipchitz space [math] is discussed. Moreover, a necessary condition of restricted boundedness of the iterated commutator [math] on weighted Lebesgue spaces via functions in [math], and an equivalent characterization of the compactness for [math] via functions in [math] are obtained. Some results are new even in the unweighted setting for the first-order commutators.
      Citation: Analysis and Applications
      PubDate: 2021-11-27T08:00:00Z
      DOI: 10.1142/S0219530521500226
       
  • A new binary representation method for shape convexity and application to
           image segmentation

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      Authors: Shousheng Luo, Xue-Cheng Tai, Yang Wang
      Pages: 1 - 17
      Abstract: Analysis and Applications, Ahead of Print.
      We present a novel and computable characterization method for convex shapes. We prove that the shape convexity is equivalent to a quadratic constraint on the associated indicator function. Such a simple characterization method allows us to design efficient algorithms for various applications with convex shape prior. In order to show the effectiveness of the proposed approach, this method is incorporated with a probability-based model to extract an object with convexity prior. The Lagrange multiplier method is used to solve the proposed model. Numerical results on various images show the superiority of the proposed method.
      Citation: Analysis and Applications
      PubDate: 2021-11-25T08:00:00Z
      DOI: 10.1142/S0219530521500238
       
  • Sparse representation of approximation to identity

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      Authors: Wei Qu, Charles K. Chui, Guan-Tie Deng, Tao Qian
      Pages: 1 - 23
      Abstract: Analysis and Applications, Ahead of Print.
      The Dirac-[math] distribution may be realized through sequences of convolutions, the latter being also regarded as approximation to the identity. This paper proposes the real form of pre-orthogonal adaptive Fourier decomposition (POAFD) method to realize fast approximation to the identity. It belongs to sparse representation of signals having potential applications in signal and image analysis.
      Citation: Analysis and Applications
      PubDate: 2021-11-24T08:00:00Z
      DOI: 10.1142/S0219530521500251
       
  • On the global [math] mapping property for Fourier integral operators

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      Authors: Guangqing Wang, Wenyi Chen, Jie Yang
      Pages: 1 - 15
      Abstract: Analysis and Applications, Ahead of Print.
      Let [math] be a Fourier integral operator defined with [math] and [math] satisfying the strong non-degenerate condition. It is shown that [math] is a bounded operator from [math] to [math], if [math], [math], [math] and m ≤ ϱ − n 2 +min 0, n 2 (ϱ − δ) .
      Citation: Analysis and Applications
      PubDate: 2021-11-17T08:00:00Z
      DOI: 10.1142/S0219530521500214
       
  • On the spherical slice transform

    • Free pre-print version: Loading...

      Authors: Boris Rubin
      Pages: 1 - 15
      Abstract: Analysis and Applications, Ahead of Print.
      We study the spherical slice transform [math] which assigns to a function [math] on the unit sphere [math] in [math] the integrals of [math] over cross-sections of [math] by [math]-dimensional affine planes passing through the north pole [math]. These transforms are known when [math]. We consider all [math] and obtain an explicit formula connecting [math] with the classical [math]-plane Radon–John transform [math] on [math]. Using this connection, known facts for [math], like inversion formulas, support theorems, representation on zonal functions, and some others, are reformulated for [math].
      Citation: Analysis and Applications
      PubDate: 2021-11-17T08:00:00Z
      DOI: 10.1142/S021953052150024X
       
  • Zero inertia limit of incompressible Qian–Sheng model

    • Free pre-print version: Loading...

      Authors: Yi-Long Luo, Yangjun Ma
      Pages: 1 - 64
      Abstract: Analysis and Applications, Ahead of Print.
      The Qian–Sheng model is a system describing the hydrodynamics of nematic liquid crystals in the Q-tensor framework. When the inertial effect is included, it is a hyperbolic-type system involving a second-order material derivative coupling with forced incompressible Navier–Stokes equations. If formally letting the inertial constant [math] go to zero, the resulting system is the corresponding parabolic model. We provide the result on the rigorous justification of this limit in [math] with small initial data, which validates mathematically the parabolic Qian–Sheng model. To achieve this, an initial layer is introduced to not only overcome the disparity of the initial conditions between the hyperbolic and parabolic models, but also make the convergence rate optimal. Moreover, a novel [math]-dependent energy norm is carefully designed, which is non-negative only when [math] is small enough, and handles the difficulty brought by the second-order material derivative.
      Citation: Analysis and Applications
      PubDate: 2021-11-09T08:00:00Z
      DOI: 10.1142/S0219530521500184
       
  • Uniform asymptotics for the discrete Laguerre polynomials

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      Authors: Dan Dai, Luming Yao
      Pages: 1 - 43
      Abstract: Analysis and Applications, Ahead of Print.
      In this paper, we consider the discrete Laguerre polynomials [math] orthogonal with respect to the weight function [math] supported on the infinite nodes [math]. We focus on the “band-saturated region” situation when the parameter [math]. As [math], uniform expansions for [math] are achieved for [math] in different regions in the complex plane. Typically, the Airy-function expansions and Gamma-function expansions are derived for [math] near the endpoints of the band and the origin, respectively. The asymptotics for the normalizing coefficient [math], recurrence coefficients [math] and [math], are also obtained. Our method is based on the Deift–Zhou steepest descent method for Riemann–Hilbert problems.
      Citation: Analysis and Applications
      PubDate: 2021-10-20T07:00:00Z
      DOI: 10.1142/S0219530521500202
       
  • Exact reconstruction of extended exponential sums using rational
           approximation of their Fourier coefficients

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      Authors: Nadiia Derevianko, Gerlind Plonka
      Pages: 1 - 35
      Abstract: Analysis and Applications, Ahead of Print.
      In this paper, we derive a new recovery procedure for the reconstruction of extended exponential sums of the form [math], where the frequency parameters [math] are pairwise distinct. In order to reconstruct [math] we employ a finite set of classical Fourier coefficients of [math] with regard to a finite interval [math] with [math]. For our method, [math] Fourier coefficients [math] are sufficient to recover all parameters of [math], where [math] denotes the order of [math]. The recovery is based on the observation that for [math] the terms of [math] possess Fourier coefficients with rational structure. We employ a recently proposed stable iterative rational approximation algorithm in [Y. Nakatsukasa, O. Sète and L. N. Trefethen, The AAA Algorithm for rational approximation, SIAM J. Sci. Comput. 40(3) (2018) A1494A1522]. If a sufficiently large set of [math] Fourier coefficients of [math] is available (i.e. [math]), then our recovery method automatically detects the number [math] of terms of [math], the multiplicities [math] for [math], as well as all parameters [math], [math], and [math], [math], [math], determining [math]. Therefore, our method provides a new stable alternative to the known numerical approaches for the recovery of exponential sums that are based on Prony’s method.
      Citation: Analysis and Applications
      PubDate: 2021-10-15T07:00:00Z
      DOI: 10.1142/S0219530521500196
       
  • The exponential behavior and stabilizability of quasilinear parabolic
           stochastic partial differential equation

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      Authors: Xiuwei Yin, Guangjun Shen, Jiang-Lun Wu
      Pages: 1 - 13
      Abstract: Analysis and Applications, Ahead of Print.
      In this paper, we study the stability of quasilinear parabolic stochastic partial differential equations with multiplicative noise, which are neither monotone nor locally monotone. The exponential mean square stability and pathwise exponential stability of the solutions are established. Moreover, under certain hypothesis on the stochastic perturbations, pathwise exponential stability can be derived, without utilizing the mean square stability.
      Citation: Analysis and Applications
      PubDate: 2021-08-23T07:00:00Z
      DOI: 10.1142/S0219530521500172
       
  • Modified proximal symmetric ADMMs for multi-block separable convex
           optimization with linear constraints

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      Authors: Yuan Shen, Yannian Zuo, Liming Sun, Xiayang Zhang
      Pages: 1 - 28
      Abstract: Analysis and Applications, Ahead of Print.
      We consider the linearly constrained separable convex optimization problem whose objective function is separable with respect to [math] blocks of variables. A bunch of methods have been proposed and extensively studied in the past decade. Specifically, a modified strictly contractive Peaceman–Rachford splitting method (SC-PRCM) [S. H. Jiang and M. Li, A modified strictly contractive Peaceman–Rachford splitting method for multi-block separable convex programming, J. Ind. Manag. Optim. 14(1) (2018) 397-412] has been well studied in the literature for the special case of [math]. Based on the modified SC-PRCM, we present modified proximal symmetric ADMMs (MPSADMMs) to solve the multi-block problem. In MPSADMMs, all subproblems but the first one are attached with a simple proximal term, and the multipliers are updated twice. At the end of each iteration, the output is corrected via a simple correction step. Without stringent assumptions, we establish the global convergence result and the [math] convergence rate in the ergodic sense for the new algorithms. Preliminary numerical results show that our proposed algorithms are effective for solving the linearly constrained quadratic programming and the robust principal component analysis problems.
      Citation: Analysis and Applications
      PubDate: 2021-08-16T07:00:00Z
      DOI: 10.1142/S0219530521500160
       
  • A mass-conserved tumor invasion system with quasi-variational degenerate
           diffusion

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      Authors: Akio Ito
      Pages: 1 - 66
      Abstract: Analysis and Applications, Ahead of Print.
      This paper deals with a nonlinear system (S) composed of three PDEs and one ODE below: (S) ut = ∇⋅ (du(α(v),w)∇β(w; u)) −∇⋅ (u∇λ(v)),vt = dvΔv + awz,wt = −awz,zt = dzΔz − bz + cu. The system (S) was proposed as one of the mathematical models which describe tumor invasion phenomena with chemotaxis effects. The most important and interesting point is that the diffusion coefficient of tumor cells, denoted by [math], is influenced by both nonlocal effect of a chemical attractive substance, denoted by [math], and the local one of extracellular matrix, denoted by [math]. From this point, the first PDE in (S) contains a nonlinear cross diffusion. Actually, this mathematical setting gives an inner product of a suitable real Hilbert space, which governs the dynamics of the density of tumor cells [math], a quasi-variational structure. Hence, the first purpose in this paper is to make it clear what this real Hilbert space is. After this, we show the existence of strong time local solutions to the initial-boundary problems associated with (S) when the space dimension is [math] by applying the general theory of evolution inclusions on real Hilbert spaces with quasi-variational structures. Moreover, for the case [math] we succeed in constructing a strong time global solution.
      Citation: Analysis and Applications
      PubDate: 2021-08-04T07:00:00Z
      DOI: 10.1142/S0219530521500159
       
  • Solitary waves and excited states for Boson stars

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      Authors: M. Melgaard, F. D. Y. Zongo
      Pages: 1 - 18
      Abstract: Analysis and Applications, Ahead of Print.
      We study the nonlinear, nonlocal, time-dependent partial differential equation i∂tφ = (−Δ + m2 − m)φ − 1 x ∗ φ 2 φ on ℝ3, which is known to describe the dynamics of quasi-relativistic boson stars in the mean-field limit. For positive mass parameter [math] we establish existence of infinitely many (corresponding to distinct energies [math]) traveling solitary waves, [math], with speed [math], where [math] corresponds to the speed of light in our choice of units. These traveling solitary waves cannot be obtained by applying a Lorentz boost to a solitary wave at rest (with [math]) because Lorentz covariance fails. Instead, we study a suitable variational problem for which the functions [math] arise as solutions (called boosted excited states) to a Choquard-type equation in [math], where the negative Laplacian is replaced by the pseudo-differential operator [math] and an additional term [math] enters. Moreover, we give a new proof for existence of boosted ground states. The results are based on perturbation methods in critical point theory.
      Citation: Analysis and Applications
      PubDate: 2021-07-26T07:00:00Z
      DOI: 10.1142/S0219530521500147
       
  • Neural network interpolation operators activated by smooth ramp functions

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      Authors: Yunyou Qian, Dansheng Yu
      Pages: 1 - 23
      Abstract: Analysis and Applications, Ahead of Print.
      In this paper, we introduce some neural network interpolation operators activated by smooth ramp functions. By using the smoothness of the ramp functions, we can give some useful estimates of the derivatives of the neural networks, which combining with some techniques in approximation theory enable us to establish the converse estimates of approximation by neural networks. We establish both the direct and the converse results of approximation by the new neural network operators defined by us, and thus give the essential approximation rate. To improve the approximation rate for functions of smoothness, we further introduce linear combinations of the new operators. The new combinations interpolate the objective function and its derivative. We also estimate the uniform convergence rate and simultaneous approximation rate by the new combinations.
      Citation: Analysis and Applications
      PubDate: 2021-07-09T07:00:00Z
      DOI: 10.1142/S0219530521500123
       
  • Summability of Fourier transforms on mixed-norm Lebesgue spaces via
           associated Herz spaces

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      Authors: Long Huang, Ferenc Weisz, Dachun Yang, Wen Yuan
      Pages: 1 - 50
      Abstract: Analysis and Applications, Ahead of Print.
      Let [math], [math] be the mixed-norm Lebesgue space, and [math] an integrable function. In this paper, via establishing the boundedness of the mixed centered Hardy–Littlewood maximal operator [math] from [math] to itself or to the weak mixed-norm Lebesgue space [math] under some sharp assumptions on [math] and [math], the authors show that the [math]-mean of [math] converges to [math] almost everywhere over the diagonal if the Fourier transform [math] of [math] belongs to some mixed-norm homogeneous Herz space [math] with [math] being the conjugate index of [math]. Furthermore, by introducing another mixed-norm homogeneous Herz space and establishing a characterization of this Herz space, the authors then extend the above almost everywhere convergence of [math]-means to the unrestricted case. Finally, the authors show that the [math]-mean of [math] converges over the diagonal to [math] at all its [math]-Lebesgue points if and only if [math] belongs to [math], and a similar conclusion also holds true for the unrestricted convergence at strong [math]-Lebesgue points. Observe that, in all these results, those Herz spaces to which [math] belongs prove to be the best choice in some sense.
      Citation: Analysis and Applications
      PubDate: 2021-06-29T07:00:00Z
      DOI: 10.1142/S0219530521500135
       
  • Invariance of the Fredholm index and spectrum of non-smooth
           pseudodifferential operators

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      Authors: Helmut Abels, Christine Pfeuffer
      Pages: 1 - 36
      Abstract: Analysis and Applications, Ahead of Print.
      In this paper, we show the invariance of the Fredholm index of non-smooth pseudodifferential operators with coefficients in Hölder spaces. By means of this invariance, we improve previous spectral invariance results for non-smooth pseudodifferential operators [math] with coefficients in Hölder spaces. For this purpose, we approximate [math] with smooth pseudodifferential operators and use a spectral invariance result of smooth pseudodifferential operators. Then, we get the spectral invariance result in analogy to a proof of the spectral invariance result for non-smooth differential operators by Rabier.
      Citation: Analysis and Applications
      PubDate: 2021-06-26T07:00:00Z
      DOI: 10.1142/S0219530521500111
       
  • Integral and series representations of [math]-Polynomials and functions:
           Part III theta, Ramanujan and [math]-Bessel functions

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      Authors: Mourad E. H. Ismail, Ruiming Zhang
      Pages: 1 - 18
      Abstract: Analysis and Applications, Ahead of Print.
      In this paper, we use an identity connecting a modified [math]-Bessel function and a [math] function to give [math]-versions of entries in the Lost Notebook of Ramanujan. We also establish an identity which gives an [math]-version of a partition identity. We prove new relations and identities involving theta functions, the Ramanujan function, the Stieltjes–Wigert, [math]-Lommel and [math]-Bessel polynomials. We introduce and study [math]-analogues of the spherical Bessel functions.
      Citation: Analysis and Applications
      PubDate: 2021-05-31T07:00:00Z
      DOI: 10.1142/S0219530521500093
       
  • Analysis of simultaneous inpainting and geometric separation based on
           sparse decomposition

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      Authors: Van Tiep Do, Ron Levie, Gitta Kutyniok
      Pages: 1 - 50
      Abstract: Analysis and Applications, Ahead of Print.
      Natural images are often the superposition of various parts of different geometric characteristics. For instance, an image might be a mixture of cartoon and texture structures. In addition, images are often given with missing data. In this paper, we develop a method for simultaneously decomposing an image to its two underlying parts and inpainting the missing data. Our separation–inpainting method is based on an [math] minimization approach, using two dictionaries, each sparsifying one of the image parts but not the other. We introduce a comprehensive convergence analysis of our method, in a general setting, utilizing the concepts of joint concentration, clustered sparsity, and cluster coherence. As the main application of our theory, we consider the problem of separating and inpainting an image to a cartoon and texture parts.
      Citation: Analysis and Applications
      PubDate: 2021-05-07T07:00:00Z
      DOI: 10.1142/S021953052150007X
       
  • Global existence and large time behavior of strong solutions to the
           nonhomogeneous heat conducting magnetohydrodynamic equations with large
           initial data and vacuum

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      Authors: Xin Zhong
      Pages: 1 - 27
      Abstract: Analysis and Applications, Ahead of Print.
      We investigate an initial boundary value problem of two-dimensional nonhomogeneous heat conducting magnetohydrodynamic equations. We prove that there exists a unique global strong solution. Moreover, we also obtain the large time decay rates of the solution. Note that the initial data can be arbitrarily large and the initial density allows vacuum states. Our method relies upon the delicate energy estimates and Desjardins’ interpolation inequality (B. Desjardins, Regularity results for two-dimensional flows of multiphase viscous fluids, Arch. Rational Mech. Anal. 137(2) (1997) 135–158).
      Citation: Analysis and Applications
      PubDate: 2021-04-30T07:00:00Z
      DOI: 10.1142/S0219530521500056
       
  • Learning theory of minimum error entropy under weak moment conditions

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      Authors: Shouyou Huang, Yunlong Feng, Qiang Wu
      Pages: 1 - 19
      Abstract: Analysis and Applications, Ahead of Print.
      Minimum error entropy (MEE) is an information theoretic learning approach that minimizes the information contained in the prediction error, which is measured by entropy. It has been successfully used in various machine learning tasks for its robustness to heavy-tailed distributions and outliers. In this paper, we consider its use in nonparametric regression and analyze its generalization performance from a learning theory perspective by imposing a [math]th order moment condition on the noise variable. To this end, we establish a comparison theorem to characterize the relation between the excess generalization error and the prediction error. A relaxed Bernstein condition and concentration inequalities are used to derive error bounds and learning rates. Note that the [math]th moment condition is rather weak particularly when [math] because the noise variable does not even admit a finite variance in this case. Therefore, our analysis explains the robustness of MEE in the presence of heavy-tailed distributions.
      Citation: Analysis and Applications
      PubDate: 2021-03-06T08:00:00Z
      DOI: 10.1142/S0219530521500044
       
  • A machine learning approach to optimal Tikhonov regularization I: Affine
           manifolds

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      Authors: Ernesto de Vito, Massimo Fornasier, Valeriya Naumova
      Pages: 1 - 48
      Abstract: Analysis and Applications, Ahead of Print.
      Despite a variety of available techniques, such as discrepancy principle, generalized cross validation, and balancing principle, the issue of the proper regularization parameter choice for inverse problems still remains one of the relevant challenges in the field. The main difficulty lies in constructing an efficient rule, allowing to compute the parameter from given noisy data without relying either on any a priori knowledge of the solution, noise level or on the manual input. In this paper, we propose a novel method based on a statistical learning theory framework to approximate the high-dimensional function, which maps noisy data to the optimal Tikhonov regularization parameter. After an offline phase where we observe samples of the noisy data-to-optimal parameter mapping, an estimate of the optimal regularization parameter is computed directly from noisy data. Our assumptions are that ground truth solutions of the inverse problem are statistically distributed in a concentrated manner on (lower-dimensional) linear subspaces and the noise is sub-gaussian. We show that for our method to be efficient, the number of previously observed samples of the noisy data-to-optimal parameter mapping needs to scale at most linearly with the dimension of the solution subspace. We provide explicit error bounds on the approximation accuracy from noisy data of unobserved optimal regularization parameters and ground truth solutions. Even though the results are more of theoretical nature, we present a recipe for the practical implementation of the approach. We conclude with presenting numerical experiments verifying our theoretical results and illustrating the superiority of our method with respect to several state-of-the-art approaches in terms of accuracy or computational time for solving inverse problems of various types.
      Citation: Analysis and Applications
      PubDate: 2021-01-30T08:00:00Z
      DOI: 10.1142/S0219530520500220
       
 
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