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Convergence and worst-case complexity of adaptive Riemannian trust-region methods for optimization on manifolds J. Glob. Optim. (IF 1.8) Pub Date : 2024-03-18 Zhou Sheng, Gonglin Yuan
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Simple proximal-type algorithms for equilibrium problems J. Glob. Optim. (IF 1.8) Pub Date : 2024-03-14 Yonghong Yao, Abubakar Adamu, Yekini Shehu, Jen-Chih Yao
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A nonmonotone accelerated proximal gradient method with variable stepsize strategy for nonsmooth and nonconvex minimization problems J. Glob. Optim. (IF 1.8) Pub Date : 2024-03-05 Hongwei Liu, Ting Wang, Zexian Liu
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Computing the recession cone of a convex upper image via convex projection J. Glob. Optim. (IF 1.8) Pub Date : 2024-03-01 Gabriela Kováčová, Firdevs Ulus
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Sketch-based multiplicative updating algorithms for symmetric nonnegative tensor factorizations with applications to face image clustering J. Glob. Optim. (IF 1.8) Pub Date : 2024-03-01
Abstract Nonnegative tensor factorizations (NTF) have applications in statistics, computer vision, exploratory multi-way data analysis, and blind source separation. This paper studies randomized multiplicative updating algorithms for symmetric NTF via random projections and random samplings. For random projections, we consider two methods to generate the random matrix and analyze the computational
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A performance analysis of Basin hopping compared to established metaheuristics for global optimization J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-28 Marco Baioletti, Valentino Santucci, Marco Tomassini
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Fast deterministic algorithms for non-submodular maximization with strong performance guarantees J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-22 Cheng Lu, Wenguo Yang
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A new dual-based cutting plane algorithm for nonlinear adjustable robust optimization J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-22
Abstract This paper explores a class of nonlinear Adjustable Robust Optimization (ARO) problems, containing here-and-now and wait-and-see variables, with uncertainty in the objective function and constraints. By applying Fenchel’s duality on the wait-and-see variables, we obtain an equivalent dual reformulation, which is a nonlinear static robust optimization problem. Using the dual formulation, we
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Consensus-based optimization for multi-objective problems: a multi-swarm approach J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-15 Kathrin Klamroth, Michael Stiglmayr, Claudia Totzeck
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A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-15 Sabah Bushaj, İ. Esra Büyüktahtakın
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Regret analysis of an online majorized semi-proximal ADMM for online composite optimization J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-15 Zehao Xiao, Liwei Zhang
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A criterion-space branch-reduction-bound algorithm for solving generalized multiplicative problems J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-15 Hongwei Jiao, Binbin Li, Wenqiang Yang
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A method for searching for a globally optimal k-partition of higher-dimensional datasets J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-13
Abstract The problem of finding a globally optimal k-partition of a set \(\mathcal {A}\) is a very intricate optimization problem for which in general, except in the case of one-dimensional data, i.e., for data with one feature ( \(\mathcal {A}\subset \mathbb {R}\) ), there is no method to solve. Only in the one-dimensional case, there are efficient methods based on the fact that the search for a
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Global solution of quadratic problems using interval methods and convex relaxations J. Glob. Optim. (IF 1.8) Pub Date : 2024-02-12 Sourour Elloumi, Amélie Lambert, Bertrand Neveu, Gilles Trombettoni
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Efficiency conditions and duality for multiobjective semi-infinite programming problems on Hadamard manifolds J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-31 Balendu Bhooshan Upadhyay, Arnav Ghosh, Savin Treanţă
This paper is devoted to the study of a class of multiobjective semi-infinite programming problems on Hadamard manifolds (in short, (MOSIP-HM)). We derive some alternative theorems analogous to Tucker’s theorem, Tucker’s first and second existence theorem, and Motzkin’s theorem of alternative in the framework of Hadamard manifolds. We employ Motzkin’s theorem of alternative to establish necessary and
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The appeals of quadratic majorization–minimization J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-28 Marc C. Robini, Lihui Wang, Yuemin Zhu
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A strong P-formulation for global optimization of industrial water-using and treatment networks J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-25 Xin Cheng, Xiang Li
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Generalized derivatives of optimal-value functions with parameterized convex programs embedded J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-25 Yingkai Song, Paul I. Barton
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Gaining or losing perspective for convex multivariate functions on a simplex J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-22 Luze Xu, Jon Lee
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Robust second order cone conditions and duality for multiobjective problems under uncertainty data J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-22 Cao Thanh Tinh, Thai Doan Chuong
This paper studies a class of multiobjective convex polynomial problems, where both the constraint and objective functions involve uncertain parameters that reside in ellipsoidal uncertainty sets. Employing the robust deterministic approach, we provide necessary conditions and sufficient conditions, which are exhibited in relation to second order cone conditions, for robust (weak) Pareto solutions
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First- and second-order optimality conditions of nonsmooth sparsity multiobjective optimization via variational analysis J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-22 Jiawei Chen, Huasheng Su, Xiaoqing Ou, Yibing Lv
In this paper, we investigate optimality conditions of nonsmooth sparsity multiobjective optimization problem (shortly, SMOP) by the advanced variational analysis. We present the variational analysis characterizations, such as tangent cones, normal cones, dual cones and second-order tangent set, of the sparse set, and give the relationships among the sparse set and its tangent cones and second-order
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Interval constraint programming for globally solving catalog-based categorical optimization J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-22
Abstract In this article, we propose an interval constraint programming method for globally solving catalog-based categorical optimization problems. It supports catalogs of arbitrary size and properties of arbitrary dimension, and does not require any modeling effort from the user. A novel catalog-based contractor (or filtering operator) guarantees consistency between the categorical properties and
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Single-lot, lot-streaming problem for a 1 + m hybrid flow shop J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-12 Sanchit Singh, Subhash C. Sarin, Ming Cheng
In this paper, we consider an application of lot-streaming for processing a lot of multiple items in a hybrid flow shop (HFS) for the objective of minimizing makespan. The HFS that we consider consists of two stages with a single machine available for processing in Stage 1 and m identical parallel machines in Stage 2. We call this problem a 1 + m TSHFS-LSP (two-stage hybrid flow shop, lot streaming
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On semidefinite programming relaxations for a class of robust SOS-convex polynomial optimization problems J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-06 Xiangkai Sun, Jiayi Huang, Kok Lay Teo
In this paper, we deal with a new class of SOS-convex (sum of squares convex) polynomial optimization problems with spectrahedral uncertainty data in both the objective and constraints. By using robust optimization and a weighted-sum scalarization methodology, we first present the relationship between robust solutions of this uncertain SOS-convex polynomial optimization problem and that of its corresponding
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Spectral projected subgradient method with a 1-memory momentum term for constrained multiobjective optimization problem J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-05 Jing-jing Wang, Li-ping Tang, Xin-min Yang
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The exact projective penalty method for constrained optimization J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-03
Abstract A new exact projective penalty method is proposed for the equivalent reduction of constrained optimization problems to nonsmooth unconstrained ones. In the method, the original objective function is extended to infeasible points by summing its value at the projection of an infeasible point on the feasible set with the distance to the projection. Beside Euclidean projections, also a pointed
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Determining solution set of nonlinear inequalities using space-filling curves for finding working spaces of planar robots J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-02 Daniela Lera, Maria Chiara Nasso, Mikhail Posypkin, Yaroslav D. Sergeyev
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Constrained multiobjective optimization of expensive black-box functions using a heuristic branch-and-bound approach J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-02
Abstract While constrained, multiobjective optimization is generally very difficult, there is a special case in which such problems can be solved with a simple, elegant branch-and-bound algorithm. This special case is when the objective and constraint functions are Lipschitz continuous with known Lipschitz constants. Given these Lipschitz constants, one can compute lower bounds on the functions over
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DC-programming for neural network optimizations J. Glob. Optim. (IF 1.8) Pub Date : 2024-01-02
Abstract We discuss two key problems related to learning and optimization of neural networks: the computation of the adversarial attack for adversarial robustness and approximate optimization of complex functions. We show that both problems can be cast as instances of DC-programming. We give an explicit decomposition of the corresponding functions as differences of convex functions (DC) and report
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Extragradient-type methods with $$\mathcal {O}\left( 1/k\right) $$ last-iterate convergence rates for co-hypomonotone inclusions J. Glob. Optim. (IF 1.8) Pub Date : 2023-12-16 Quoc Tran-Dinh
We develop two “Nesterov’s accelerated” variants of the well-known extragradient method to approximate a solution of a co-hypomonotone inclusion constituted by the sum of two operators, where one is Lipschitz continuous and the other is possibly multivalued. The first scheme can be viewed as an accelerated variant of Tseng’s forward-backward-forward splitting (FBFS) method, while the second one is
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A Bregman inertial forward-reflected-backward method for nonconvex minimization J. Glob. Optim. (IF 1.8) Pub Date : 2023-12-16 Xianfu Wang, Ziyuan Wang
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Global optimization of mixed-integer nonlinear programs with SCIP 8 J. Glob. Optim. (IF 1.8) Pub Date : 2023-12-14 Ksenia Bestuzheva, Antonia Chmiela, Benjamin Müller, Felipe Serrano, Stefan Vigerske, Fabian Wegscheider
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A surrogate-assisted evolutionary algorithm with clustering-based sampling for high-dimensional expensive blackbox optimization J. Glob. Optim. (IF 1.8) Pub Date : 2023-12-14 Fusheng Bai, Dongchi Zou, Yutao Wei
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Variable sample-size optimistic mirror descent algorithm for stochastic mixed variational inequalities J. Glob. Optim. (IF 1.8) Pub Date : 2023-12-11 Zhen-Ping Yang, Yong Zhao, Gui-Hua Lin
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On asymptotic convergence rate of random search J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-24 Dawid Tarłowski
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On inexact versions of a quasi-equilibrium problem: a Cournot duopoly perspective J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-24 E. L. Dias Júnior, P. J. S. Santos, A. Soubeyran, J. C. O. Souza
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Existence of solutions to $$\Gamma $$ -robust counterparts of gap function formulations of uncertain LCPs with ellipsoidal uncertainty sets J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-24 Lulin Tan, Wei Hong Yang, Jinbiao Pan
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Discrete approximation for two-stage stochastic variational inequalities J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-14 Jie Jiang, Hailin Sun
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Strict feasibility for the polynomial complementarity problem J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-14 Xue-liu Li, Guo-ji Tang
In the present paper, the strict feasibility of the polynomial complementarity problem (PCP) is investigated. To this end, as a generalization of the concept of S-tensor, a concept of S-tensor tuple is introduced. Some properties of S-tensor tuples are investigated. In particular, several conditions are proposed to judge whether a tensor tuple is an S-tensor tuple or not. Then, based on the S-tensor
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Separating disconnected quadratic level sets by other quadratic level sets J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-13 Huu-Quang Nguyen, Ya-Chi Chu, Ruey-Lin Sheu
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Configuring an heterogeneous smartgrid network: complexity and approximations for tree topologies J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-14 Dominique Barth, Thierry Mautor, Dimitri Watel, Marc-Antoine Weisser
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A semi-Bregman proximal alternating method for a class of nonconvex problems: local and global convergence analysis J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-03 Eyal Cohen, D. Russell Luke, Titus Pinta, Shoham Sabach, Marc Teboulle
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Heteroscedastic Bayesian optimization using generalized product of experts J. Glob. Optim. (IF 1.8) Pub Date : 2023-11-04 Saulius Tautvaišas, Julius Žilinskas
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Globalized distributionally robust optimization based on samples J. Glob. Optim. (IF 1.8) Pub Date : 2023-10-06 Yueyao Li, Wenxun Xing
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DC algorithms in Hilbert spaces and the solution of indefinite infinite-dimensional quadratic programs J. Glob. Optim. (IF 1.8) Pub Date : 2023-10-03 Yongdo Lim, Hoang Ngoc Tuan, Nguyen Dong Yen
This paper considers the DC (Difference-of-Convex-functions) algorithms in a Hilbert space setting and discusses their convergence. Applied to indefinite quadratic programs under linear constraints in Hilbert spaces, among other things, the DCA yields two basic types of iteration algorithms, called the Projection DC decomposition algorithm and the Proximal DC decomposition algorithm. It is proved that
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Small polygons with large area J. Glob. Optim. (IF 1.8) Pub Date : 2023-09-29 Christian Bingane, Michael J. Mossinghoff
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A bundle-type method for nonsmooth DC programs J. Glob. Optim. (IF 1.8) Pub Date : 2023-09-25 Christian Kanzow, Tanja Neder
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A utopia point method-based robust vector polynomial optimization scheme J. Glob. Optim. (IF 1.8) Pub Date : 2023-09-21 Tianyi Han, Liguo Jiao, Jae Hyoung Lee, Junping Yin
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Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems J. Glob. Optim. (IF 1.8) Pub Date : 2023-09-21 Zhiming Zhong, Neng Fan, Lei Wu
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Optimality and error bound for set optimization with application to uncertain multi-objective programming J. Glob. Optim. (IF 1.8) Pub Date : 2023-09-19 Wenyan Han, Guolin Yu
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Proximal gradient algorithm with trust region scheme on Riemannian manifold J. Glob. Optim. (IF 1.8) Pub Date : 2023-09-16 Shimin Zhao, Tao Yan, Yuanguo Zhu
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Time-dependent elliptic quasi-variational-hemivariational inequalities: well-posedness and application J. Glob. Optim. (IF 1.8) Pub Date : 2023-09-04 Tie-jun Jiang, Dong-ling Cai, Yi-bin Xiao, Stanisław Migórski
In this paper we investigate a class of time-dependent quasi-variational-hemivariational inequalities (TDQVHVIs) of elliptic type in a reflexive separable Banach space, which is characterized by a constraint set depending on a solution. The solvability of the TDQVHVIs is obtained by employing a measurable selection theorem for measurable set-valued mappings, while the uniqueness of solution to the
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A singular value shrinkage thresholding algorithm for folded concave penalized low-rank matrix optimization problems J. Glob. Optim. (IF 1.8) Pub Date : 2023-08-22 Xian Zhang, Dingtao Peng, Yanyan Su
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Constraint generation approaches for submodular function maximization leveraging graph properties J. Glob. Optim. (IF 1.8) Pub Date : 2023-08-17 Eszter Csókás, Tamás Vinkó
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Approximation hierarchies for copositive cone over symmetric cone and their comparison J. Glob. Optim. (IF 1.8) Pub Date : 2023-08-11 Mitsuhiro Nishijima, Kazuhide Nakata
We first provide an inner-approximation hierarchy described by a sum-of-squares (SOS) constraint for the copositive (COP) cone over a general symmetric cone. The hierarchy is a generalization of that proposed by Parrilo (Structured semidefinite programs and semialgebraic geometry methods in Robustness and optimization, Ph.D. Thesis, California Institute of Technology, Pasadena, CA, 2000) for the usual
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Directional shadow price in linearly constrained nonconvex optimization models J. Glob. Optim. (IF 1.8) Pub Date : 2023-08-11 Tao Jie, Gao Yan
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Improving exploration strategies in large dimensions and rate of convergence of global random search algorithms J. Glob. Optim. (IF 1.8) Pub Date : 2023-07-24 Jack Noonan, Anatoly Zhigljavsky
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Global optimization via optimal decision trees J. Glob. Optim. (IF 1.8) Pub Date : 2023-07-21 Dimitris Bertsimas, Berk Öztürk
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IPRQP: a primal-dual interior-point relaxation algorithm for convex quadratic programming J. Glob. Optim. (IF 1.8) Pub Date : 2023-07-20 Rui-Jin Zhang, Xin-Wei Liu, Yu-Hong Dai
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Relaxed method for optimization problems with cardinality constraints J. Glob. Optim. (IF 1.8) Pub Date : 2023-07-18 Yan-Chao Liang, Gui-Hua Lin