Results 1 to 10 of about 1,159 (105)
Frontiers in Applied Mathematics and Statistics, 2022 In this work, a new class of spectral conjugate gradient (CG) method is proposed for solving unconstrained optimization models. The search direction of the new method uses the ZPRP and JYJLL CG coefficients.
Fevi Novkaniza +3 more
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The exactness of the ℓ1 penalty function for a class of mathematical programs with generalized complementarity constraints [PDF]
Fundamental ResearchIn a mathematical program with generalized complementarity constraints (MPGCC), complementarity relation is imposed between each pair of variable blocks.
Yukuan Hu, Xin Liu
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The Bregman Proximal Average [PDF]
SIAM Journal on Optimization, 2021 We provide a proximal average with repect to a 1-coercive Legendre function. In the sense of Bregman distance, the Bregman envelope of the proximal average is a convex combination of Bregman envelopes of individual functions. The Bregman proximal mapping
Xianfu Wang, Heinz H. Bauschke
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Miskolc Mathematical Notes, 2021 In this paper, a new class of nonconvex nonsmooth multiobjective programming problems with both inequality and equality constraints defined in a real Banach space is considered.
T. Antczak, R. Verma
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KT-E-invexity in E-differentiable vector optimization problems
, 2021 In this paper, a new concept of generalized convexity is introduced for E-differentiable vector optimization problems. Namely, the concept of KT-E-invexity is defined for (not necessarily) differentiable vector optimization problems in which the ...
Najeeb Abdulaleem
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An improved Jaya optimization algorithm with ring topology and population size reduction
Journal of Intelligent Systems, 2022 An improved variant of the Jaya optimization algorithm, called Jaya2, is proposed to enhance the performance of the original Jaya sacrificing its algorithmic design. The proposed approach arranges the solutions in a ring topology to reduce the likelihood
Omran Mahamed G. H., Iacca Giovanni
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, 2021 Robust Optimization (RO) arises in two stages of optimization, first level for maximizing over the uncertain data and second level for minimizing over the feasible set.
M. Barro +3 more
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Solving polyhedral d.c. optimization problems via concave minimization [PDF]
, 2020 The problem of minimizing the difference of two convex functions is called polyhedral d.c. optimization problem if at least one of the two component functions is polyhedral.
Dahl, Simeon vom, Löhne, Andreas
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A new conjugate gradient method for acceleration of gradient descent algorithms
Moroccan Journal of Pure and Applied Analysis, 2021 An accelerated of the steepest descent method for solving unconstrained optimization problems is presented. which propose a fundamentally different conjugate gradient method, in which the well-known parameter βk is computed by an new formula.
Rahali Noureddine +2 more
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Outcome space range reduction method for global optimization of sum of affine ratios problem
Open Mathematics, 2016 Many algorithms for globally solving sum of affine ratios problem (SAR) are based on equivalent problem and branch-and-bound framework. Since the exhaustiveness of branching rule leads to a significant increase in the computational burden for solving the
Jiao Hongwei +3 more
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