|
|
- A hybrid acceleration Douglas-Rachford splitting method for solving
large-scale absolute value equations-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: In this paper, we propose a hybrid acceleration Douglas-Rachford splitting method for solving large-scale absolute value equations of the form $$Ax- x...
PubDate: 2025-06-07
- Optimal planning of renewable-based mining microgrids: a comparative study
of multi-objective evolutionary algorithms-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: The design of renewable-powered mining microgrids is an essential study for the shift towards electric and carbon-neutral operations within the mining sector. This paper presents a multi-objective two-stage op...
PubDate: 2025-06-05
- Learning to deactivate probing with graph convolutional network for
mixed-integer nonlinear programming-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Global solvers for mixed-integer nonlinear programming problems widely apply probing to enhance domain reduction, identify implications, and detect conflicts. The probing technique tentatively restricts variable bounds and derives valuable insights through performing bound propagations or by solving subproblems. However, due to its high complexity, solvers design specific rules to limit probing and apply various conditions when selecting probing variables and ranges. In this work, we propose representing a general mixed-integer nonlinear programming problem with a tripartite graph, which generates features to capture the neighborhood structure around variables, constraints, and nonlinear expressions. Equipped with properties like variable bounds and integrality conditions, a graph convolutional network is trained to decide whether to deactivate probing. Compared to classical binary classification models based on structural statistics, our computational experiments on benchmark libraries demonstrate that the graph-based policy provides greater robustness, reduces solution times, and exhibits promising generalization capabilities for broader learning tasks.
PubDate: 2025-06-03
- Improvement of convergence criteria for finding common fixed points of
multiple finite demicontractive mappings-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: This study aims to address the problem of finding a common fixed point for multiple finite demicontractive mappings. We begin by examining the fundamental characteristics of the convex combination of these mappings. Then, we propose a new approach to establish the strong convergence of Thong and Hieu’s method, even under weak convergence criteria. Notably, we have made two significant improvements compared to previous research. Our numerical experiments confirm the effectiveness of our proposed convergence criteria, resulting in substantial enhancements in the method’s performance.
PubDate: 2025-05-23
- A study on the impact of selecting the follower’s reaction in solving
semi-vectorial bilevel problems-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: A bilevel programming problem is a specific type of optimization problem that involves two hierarchical and interrelated decision levels, each associated with a leader and a follower. In this type of problem, the decisions of the leader restrict the solution space of the follower, who reacts rationally to optimize their own objective function. Furthermore, the follower’s reaction affects the objective function or constraints of the leader. To obtain bilevel feasible solutions, the follower must optimally solve its problem parameterized by the leader’s decision. When the follower considers two objectives simultaneously, determining their optimal reaction becomes unclear. Solving a follower’s bi-objective problem results in obtaining the non-dominated solutions that form the Pareto front. As a result, the follower must select one of these solutions as their rational reaction. However, this fundamental issue has received limited attention in the existing literature. In this study, our aim is to demonstrate the significant impact that the assumptions regarding the follower’s reaction can have on the bilevel problem. We will consider collaborative, adversarial, and various other approaches to illustrate their effectiveness in addressing these complex problems. The study is based on a novel variant of a competitive facility location problem, resulting in a binary bilevel problem with two objectives at the lower level. To solve this problem, we combine an enumerative scheme for the upper level with an $$\varepsilon$$-constraint method for the lower level. The numerical results clearly highlight the importance of making well-founded assumptions regarding the follower’s reaction. An analysis of these non-dominated solutions is conducted to assess bilevel feasibility. The study concludes with a critique of lenient practices in selecting the follower’s reaction.
PubDate: 2025-05-21
- Symmetric positive subdefinite tensors
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: In this paper, we introduce symmetric positive subdefinite tensors as an extension of symmetric positive semidefinite tensors. We investigate the underlying properties of these tensors and establish the equivalence of symmetric positive subdefinite and positive semidefinite tensors under an additional condition. Inspired by Martos’ research on positive subdefinite matrices, we propose a complete characterization of symmetric positive subdefinite tensors of rank one.
PubDate: 2025-05-20
- A monotonic optimization approach to mixed variational inequality problems
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: We study a nonconvex mixed variational inequality problem in $$\mathbb {R}^n$$ by using monotonic optimization approach. We show that a class of variational inequality problems can be transformed into a monotonic optimization problem and then propose a branch-reduce-and-bound algorithm as a solution approach. The convergence result of the algorithm is established under increasing assumptions on the cost and constraint functions. Applications to two equilibrium models are presented.
PubDate: 2025-05-15
- Branch-and-bound-and-memorize for the blocking permutation flowshop
problem-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Flow scheduling problems are among the most studied problems in the optimization literature and different technological constraints characterize the many variants of the general problem. This paper deals with the flowshop scheduling problem where there is no infinite buffer between subsequent machines: a job may lay on the current machine without leaving until the buffer has sufficient room. Such a situation is called blocking. In this paper, a branch-and-bound-based algorithm is proposed to optimally solve instances of the problem. The proposed method relies on a fast upper bounding procedure for early node pruning and passive node memorization that further speeds up the problem resolution. The proposed approach is compared with previous results in the literature and assessed as outperforming.
PubDate: 2025-05-12
- Local error bounds for the generalized polynomial complementarity problem
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: In this paper, we establish two local error bounds for the generalized polynomial complementarity problem. The first error bound is obtained by using the natural residual function; and the second error bound is obtained by using a residual function definited by the standard violation measure of the polynomial equalities and inequalities. These two error bounds improve the corresponding results obtained recently in the literature for the generalized polynomial complementary problem. Furthermore, when the generalized polynomial complementarity problem is reduced to a quadratic complementarity problem, the error bounds obtained also improve the corresponding results given in the literature.
PubDate: 2025-05-11
- Mixed-integer linear optimization for cardinality-constrained random
forests-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Random forests are among the most famous algorithms for solving classification problems, in particular for large-scale data sets. Considering a set of labeled points and several decision trees, the method takes the majority vote to classify a new given point. In some scenarios, however, labels are only accessible for a proper subset of the given points. Moreover, this subset can be non-representative, e.g., due to collection bias. Semi-supervised learning considers the setting of labeled and unlabeled data and often improves the reliability of the results. In addition, it can be possible to obtain additional information about class sizes from undisclosed sources. We propose a mixed-integer linear optimization model for computing a semi-supervised random forest that covers the setting of labeled and unlabeled data points as well as the overall number of points in each class for a binary classification. Since the solution time rapidly grows as the number of variables increases, we present some problem-tailored preprocessing techniques and an intuitive branching rule. Our numerical results show that our approach leads to better accuracy and a better Matthews correlation coefficient for biased samples compared to random forests by majority vote, even if only a few labeled points are available.
PubDate: 2025-05-10
- A folding preprocess for the max k-cut problem
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Given graph $$G=(V,E)$$ with vertex set V and edge set E, the max k-cut problem seeks to partition the vertex set V into at most k subsets that maximize the weight (number) of edges with endpoints in different parts. This paper proposes a graph folding procedure (i.e., a procedure that reduces the number of the vertices and edges of graph G) for the weighted max k-cut problem that may help reduce the problem’s dimensionality. While our theoretical results hold for any $$k \ge 2$$, our computational results show the effectiveness of the proposed preprocess only for $$k=2$$ and on two sets of instances. Furthermore, we observe that the preprocess improves the performance of a MIP solver on a set of large-scale instances of the max cut problem.
PubDate: 2025-04-28
- An optimization approach to coalition formation in centralized EOQ
problems with additive transportation costs-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: This paper deals with coalition formation in a centralized inventory problem involving a set of agents who place new orders accordingly to an Economic Order Quantity policy, without holding costs and additive transportation costs. In this context, the cooperation is not necessarily profitable. We introduce an optimization-based procedure to systematically identify the most efficient ordering coalition, allowing for a step-by-step determination of the most efficient coalitional structures. Several examples are used to examine the properties exhibited by the resulting coalitional structures, in line with those explored in similar contexts.
PubDate: 2025-02-24
- A generalized combination of parallel machine scheduling and path
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: This paper studies a generalized combination of parallel machine scheduling and path (GCSP), which is formalized as follows: Given a directed graph ...
PubDate: 2024-10-22
- An approximation algorithm for the k-prize-collecting hitting set
problem-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: We study the k-prize-collecting hitting set problem in hypergraphs. We first design a greedy algorithm for the k-hitting set problem with approximation ratio ...
PubDate: 2024-10-13
- On complexity constants of linear and quadratic models for derivative-free
trust-region algorithms-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Complexity analysis has become an essential tool in the convergence analysis of optimization algorithms. For derivative-free optimization algorithms, it is not different. Interestingly, several constants that ...
PubDate: 2024-09-23
- A projected fixed point method for a class of vertical tensor
complementarity problems-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: In this paper, we consider the numerical solution of a class of vertical tensor complementarity problems. By reformulating the involved vertical tensor complementarity problem (VTCP) as an equivalent projected...
PubDate: 2024-08-27
- The budgeted maximin share allocation problem
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: We are given a set of indivisible goods and a set of m agents where each good has a size and each agent has an additive valuation function and a budget. The budgeted maximin share allocation problem is to find a ...
PubDate: 2024-08-22
- Explicit iterative algorithms for solving the split equality problems in
Hilbert spaces-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: We introduce and study some explicit iterative algorithms for solving the system of split equality problems in Hilbert spaces. The strong convergence of the proposed algorithms is proved by using some milder c...
PubDate: 2024-08-20
- Convergence rate of projected subgradient method with time-varying
step-sizes-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: We establish the optimal ergodic convergence rate for the classical projected subgradient method with time-varying step-sizes. This convergence rate remains the same even if we slightly increase the weight of ...
PubDate: 2024-08-08
- Comments on finite termination of the generalized Newton method for
absolute value equations-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: We consider the generalized Newton method (GNM) for the absolute value equation (AVE) $$Ax- x =b$$
PubDate: 2024-05-20
|
Hybrid journal (It can contain Open Access articles)
