Results 11 to 20 of about 33,726 (205)
Online Semidefinite Programming. [PDF]
, 2016 We consider semidefinite programming through the lens of online algorithms - what happens if not all input is given at once, but rather iteratively? In what way does it make sense for a semidefinite program to be revealed? We answer these questions by defining a model for online semidefinite programming.
Elad, Noa +2 more
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A semidefinite program for distillable entanglement [PDF]
IEEE Transactions on Information Theory, 2001 We show that the maximum fidelity obtained by a p.p.t. distillation protocol is given by the solution to a certain semidefinite program. This gives a number of new lower and upper bounds on p.p.t. distillable entanglement (and thus new upper bounds on 2-locally distillable entanglement).
Rains, Eric M.
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Definable Ellipsoid Method, Sums-of-Squares Proofs, and the Isomorphism Problem [PDF]
, 2018 The ellipsoid method is an algorithm that solves the (weak) feasibility and linear optimization problems for convex sets by making oracle calls to their (weak) separation problem.
Atserias, Albert, Ochremiak, Joanna
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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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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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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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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 ...
Bachoc, C. +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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A hybrid constraint programming and semidefinite programming approach for the stable set problem
, 2003 This work presents a hybrid approach to solve the maximum stable set problem, using constraint and semidefinite programming. The approach consists of two steps: subproblem generation and subproblem solution.
B. Borchers +18 more
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