Zhuan Khye Koh,Laura Sanità
Zhuan Khye Koh
Cooperative games form an important class of problems in game theory, where a key goal is to distribute a value among a set of players who are allowed to cooperate by forming coalitions. An outcome of the game is given by an allocation vect...
A computational study of exact subgraph based SDP bounds for Max-Cut, stable set and coloring [0.03%]
基于精确子图的SDP界对最大割、稳定集和着色问题的计算研究
Elisabeth Gaar,Franz Rendl
Elisabeth Gaar
The "exact subgraph" approach was recently introduced as a hierarchical scheme to get increasingly tight semidefinite programming relaxations of several NP-hard graph optimization problems. Solving these relaxations is a computational chall...
Mathias Pohl,Alexander Ristig,Walter Schachermayer et al.
Mathias Pohl et al.
Understanding the structure of financial markets deals with suitably determining the functional relation between financial variables. In this respect, important variables are the trading activity, defined here as the number of trades N, the...
Chengde Qian,Quoc Tran-Dinh,Sheng Fu et al.
Chengde Qian et al.
We consider the classification problem when the input features are represented as matrices rather than vectors. To preserve the intrinsic structures for classification, a successful method is the Support Matrix Machine (SMM) in [19], which ...
Sebastian Banert,Radu Ioan Boț
Sebastian Banert
The possibilities of exploiting the special structure of d.c. programs, which consist of optimising the difference of convex functions, are currently more or less limited to variants of the DCA proposed by Pham Dinh Tao and Le Thi Hoai An i...
Sample Average Approximation with Sparsity-Inducing Penalty for High-Dimensional Stochastic Programming [0.03%]
带有稀疏诱导惩罚的高维随机规划样本平均近似法
Hongcheng Liu,Xue Wang,Tao Yao et al.
Hongcheng Liu et al.
The theory on the traditional sample average approximation (SAA) scheme for stochastic programming (SP) dictates that the number of samples should be polynomial in the number of problem dimensions in order to ensure proper optimization accu...
When are static and adjustable robust optimization problems with constraint-wise uncertainty equivalent? [0.03%]
约束条件不确定性下的静态鲁棒优化问题和可调鲁棒优化问题何时等价？
Ahmadreza Marandi,Dick den Hertog
Ahmadreza Marandi
Adjustable robust optimization (ARO) generally produces better worst-case solutions than static robust optimization (RO). However, ARO is computationally more difficult than RO. In this paper, we provide conditions under which the worst-cas...
Bounds on entanglement dimensions and quantum graph parameters via noncommutative polynomial optimization [0.03%]
纠缠维度和量子图参数的界值通过非交换多项式最优化所得
Sander Gribling,David de Laat,Monique Laurent
Sander Gribling
In this paper we study optimization problems related to bipartite quantum correlations using techniques from tracial noncommutative polynomial optimization. First we consider the problem of finding the minimal entanglement dimension of such...
Krzysztof Fleszar,Matthias Mnich,Joachim Spoerhase
Krzysztof Fleszar
We study the classical NP -hard problems of finding maximum-size subsets from given sets of k terminal pairs that can be routed via edge-disjoint paths (MaxEDP) or node-disjoint paths (MaxNDP) in a given graph. The approximability of MaxEDP...
Boris Houska,Benoît Chachuat
Boris Houska
We propose a complete-search algorithm for solving a class of non-convex, possibly infinite-dimensional, optimization problems to global optimality. We assume that the optimization variables are in a bounded subset of a Hilbert space, and w...