Results 21 to 30 of about 68 (67)
Alternative SDP and SOCP approximations for polynomial optimization
EURO Journal on Computational Optimization, 2019 In theory, hierarchies of semidefinite programming (SDP) relaxations based on sum of squares (SOS) polynomials have been shown to provide arbitrarily close approximations for a general polynomial optimization problem (POP).
Xiaolong Kuang +3 more
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A new branch and bound algorithm for minimax ratios problems
Open Mathematics, 2017 This study presents an efficient branch and bound algorithm for globally solving the minimax fractional programming problem (MFP). By introducing an auxiliary variable, an equivalent problem is firstly constructed and the convex relaxation programming ...
Zhao Yingfeng, Liu Sanyang, Jiao Hongwei
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A bounded degree SOS hierarchy for polynomial optimization
EURO Journal on Computational Optimization, 2017 We consider a new hierarchy of semidefinite relaxations for the general polynomial optimization problem (P):f∗=min{f(x):x∈K} on a compact basic semi-algebraic set K⊂Rn.
JeanB. Lasserre +2 more
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A New Filled Function for Global Optimization
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2023 The filled function method has recently become very popular in optimization theory, as it is an e cient and e ective method for finding the global minimizer of multimodal functions.
Şahiner Ahmet +2 more
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Demonstratio MathematicaIt is of strong theoretical significance and application prospects to explore three-block nonconvex optimization with nonseparable structure, which are often modeled for many problems in machine learning, statistics, and image and signal processing.
Zhao Ying, Lan Heng-you, Xu Hai-yang
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A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations
Results in Applied Mathematics, 2019 For the problem of sparse recovery, it is widely accepted that nonconvex minimizations are better than ℓ1 penalty in enhancing the sparsity of solution.
Hoang Tran, Clayton Webster
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An ADMM-based heuristic algorithm for optimization problems over nonconvex second-order cone
Open Computer ScienceThe nonconvex second-order cone (nonconvex SOC) is a nonconvex extension to the convex second-order cone, in the sense that it consists of any vector divided into two sub-vectors for which the Euclidean norm of the first sub-vector is at least as large ...
Alzalg Baha, Benakkouche Lilia
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Sufficient pruning conditions for MINLP in gas network design
EURO Journal on Computational Optimization, 2017 One-quarter of Europe’s energy demand is provided by natural gas distributed through a vast pipeline network covering the whole of Europe. At a cost of 1 million Euros per kilometer the extension of the European pipeline network is already a multi ...
Jesco Humpola, Felipe Serrano
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An inertial forward–backward algorithm for the minimization of the sum of two nonconvex functions
EURO Journal on Computational Optimization, 2016 We propose a forward–backward proximal-type algorithm with inertial/memory effects for minimizing the sum of a nonsmooth function with a smooth one in the nonconvex setting.
Radu Ioan Boţ +2 more
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Open MathematicsIn this article, we work on vector optimization problems in linear topological spaces. Our vector optimization problems have weakened convex inequality constraints and weakened affine equality constraints.
Zeng Renying
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