Results 21 to 30 of about 6,081 (225)
Enhancing pseudo-telepathy in the magic square game. [PDF]
PLoS ONE, 2013 We study the possibility of reversing an action of a quantum channel. Our principal objective is to find a specific channel that reverses as accurately as possible an action of a given quantum channel. To achieve this goal we use semidefinite programming.
Lukasz Pawela +3 more
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Entropy-Penalized Semidefinite Programming [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Low-rank methods for semi-definite programming (SDP) have gained a lot of interest recently, especially in machine learning applications. Their analysis often involves determinant-based or Schatten-norm penalties, which are difficult to implement in practice due to high computational efforts.
Mikhail Krechetov +3 more
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Strong Duality for Semidefinite Programming [PDF]
SIAM Journal on Optimization, 1997 Summary: It is well known that the duality theory for linear programming (LP) is powerful and elegant and lies behind algorithms such as simplex and interior-point methods. However, the standard Lagrangian for nonlinear programs requires constraint qualifications to avoid duality gaps.
Motakuri V. Ramana +2 more
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Open Journal of Mathematical Optimization, 2022 We address the issue of computing a global minimizer of the AC Optimal Power Flow problem. We introduce valid inequalities to strengthen the Semidefinite Programming relaxation, yielding a novel Conic Programming relaxation.
Oustry, Antoine
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A superlinearly convergent SSDP algorithm for nonlinear semidefinite programming
Journal of Inequalities and Applications, 2019 In this paper, we present a sequential semidefinite programming (SSDP) algorithm for nonlinear semidefinite programming. At each iteration, a linear semidefinite programming subproblem and a modified quadratic semidefinite programming subproblem are ...
Jian Ling Li, Hui Zhang
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Invariant Semidefinite Programs [PDF]
, 2011 In the last years many results in the area of semidefinite programming were obtained for invariant (finite dimensional, or infinite dimensional) semidefinite programs - SDPs which have symmetry. This was done for a variety of problems and applications. The purpose of this handbook chapter is to give the reader the necessary background for dealing with ...
C. Bachoc +3 more
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Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints [PDF]
Quantum, 2023 Semidefinite programs are optimization methods with a wide array of applications, such as approximating difficult combinatorial problems. One such semidefinite program is the Goemans-Williamson algorithm, a popular integer relaxation technique.
Taylor L. Patti +3 more
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Critical Multipliers in Semidefinite Programming [PDF]
Asia-Pacific Journal of Operational Research, 2020 It was proved in Izmailov and Solodov (2014). Newton-Type Methods for Optimization and Variational Problems, Springer] that the existence of a noncritical multiplier for a (smooth) nonlinear programming problem is equivalent to an error bound condition for the Karush–Kuhn–Thcker (KKT) system without any assumptions.
Tianyu Zhang, Liwei Zhang
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Gap inequalities for non-convex mixed-integer quadratic programs [PDF]
, 2011 Laurent and Poljak introduced a very general class of valid linear inequalities, called gap inequalities, for the max-cut problem. We show that an analogous class of inequalities can be defined for general non-convex mixed-integer quadratic programs ...
Galli, Laura +8 more
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A Customized ADMM Approach for Large-Scale Nonconvex Semidefinite Programming
Mathematics, 2023 We investigate a class of challenging general semidefinite programming problems with extra nonconvex constraints such as matrix rank constraints. This problem has extensive applications, including combinatorial graph problems, such as MAX-CUT and ...
Chuangchuang Sun
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