Zobrazeno 1 - 10
of 13 847
pro vyhledávání: '"Duality Gap"'
For mixed integer programs (MIPs) with block structures and coupling constraints, on dualizing the coupling constraints the resulting Lagrangian relaxation becomes decomposable into blocks which allows for the use of parallel computing. However, the
Externí odkaz:
http://arxiv.org/abs/2411.12085
Autor:
Yang, Meijia, Xia, Yong
The generalized trace ratio problem {\rm (GTRP)} is to maximize a quadratic fractional objective function in trace formulation over the Stiefel manifold. In this paper, based on a newly developed matrix S-lemma, we show that {\rm (GTRP)}, if a redund
Externí odkaz:
http://arxiv.org/abs/2411.09187
Autor:
Walwil, Iyad, Fercoq, Olivier
We optimize the running time of the primal-dual algorithms by optimizing their stopping criteria for solving convex optimization problems under affine equality constraints, which means terminating the algorithm earlier with fewer iterations. We study
Externí odkaz:
http://arxiv.org/abs/2403.12579
Autor:
Bednarczuk, Ewa, Syga, Monika
We prove sufficient and necessary conditions ensuring zero duality gap for Lagrangian duality in some classes of nonconvex optimization problems. To this aim, we use the $\Phi$-convexity theory and minimax theorems for $\Phi$-convex functions. The ob
Externí odkaz:
http://arxiv.org/abs/2401.04806
Publikováno v:
Mathematics of Operations Research, 2002 Nov 01. 27(4), 775-791.
Externí odkaz:
https://www.jstor.org/stable/3690467
Autor:
Zalinescu, C.
The aim of this paper is to revisit some duality results in conic linear programming and to answer an open problem related to the duality gap function for Gale's example.
Comment: 14 pages; some misprints are corrected
Comment: 14 pages; some misprints are corrected
Externí odkaz:
http://arxiv.org/abs/2205.12631
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Training deep neural networks is a challenging non-convex optimization problem. Recent work has proven that the strong duality holds (which means zero duality gap) for regularized finite-width two-layer ReLU networks and consequently provided an equi
Externí odkaz:
http://arxiv.org/abs/2110.06482
Despite the accomplishments of Generative Adversarial Networks (GANs) in modeling data distributions, training them remains a challenging task. A contributing factor to this difficulty is the non-intuitive nature of the GAN loss curves, which necessi
Externí odkaz:
http://arxiv.org/abs/2105.04801
This work addresses the Robust counterpart of the Shortest Path Problem (RSPP) with a correlated uncertainty set. Since this problem is hard, a heuristic approach, based on Frank-Wolfe's algorithm named Discrete Frank-Wolf (DFW), has recently been pr
Externí odkaz:
http://arxiv.org/abs/2110.15653