Results 11 to 20 of about 836,294 (206)
Scalable Semidefinite Programming [PDF]
SIAM Journal on Mathematics of Data Science, 2021 Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct randomized algorithm for solving large, weakly constrained SDP problems by economizing on the storage and arithmetic costs.
Alp Yurtsever +4 more
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Semidefinite Programming and Ramsey Numbers [PDF]
SIAM Journal on Discrete Mathematics, 2021 Finding exact Ramsey numbers is a problem typically restricted to relatively small graphs. The flag algebra method was developed to find asymptotic results for very large graphs, so it seems that the method is not suitable for finding small Ramsey numbers. But this intuition is wrong, and we will develop a technique to do just that in this paper.
Bernard Lidický, Florian Pfender
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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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Subsampling algorithms for semidefinite programming [PDF]
Stochastic Systems, 2011 We derive a stochastic gradient algorithm for semidefinite optimization using randomization techniques. The algorithm uses subsampling to reduce the computational cost of each iteration and the subsampling ratio explicitly controls granularity, i.e.
Alexandre W. d'Aspremont
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Semidefinite Programming and Integer Programming [PDF]
, 2005 We survey how semidefinite programming can be used for finding good approximative solutions to hard combinatorial optimization ...
M. Laurent (Monique), F. Rendl (Franz)
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HBSP: a hybrid bilinear and semidefinite programming approach for aligning partially overlapping point clouds [PDF]
Scientific ReportsIn many applications, there is a need for algorithms that can align partially overlapping point clouds while remaining invariant to corresponding transformations.
Wei Lian, Fei Ma, Zhesen Cui, Hang Pan
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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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Time-Varying Semidefinite Programs [PDF]
Mathematics of Operations Research, 2021 We study time-varying semidefinite programs (TV-SDPs), which are semidefinite programs whose data (and solutions) are functions of time. Our focus is on the setting where the data vary polynomially with time. We show that under a strict feasibility assumption, restricting the solutions to also be polynomial functions of time does not change the ...
Amir Ali Ahmadi, Bachir El Khadir
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Binary positive semidefinite matrices and associated integer polytopes [PDF]
, 2012 We consider the positive semidefinite (psd) matrices with binary entries, along with the corresponding integer polytopes.We begin by establishing some basic properties of these matrices and polytopes.
Sorensen, M M, Letchford, A N
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Quantum key distribution rates from semidefinite programming [PDF]
Quantum, 2023 Computing the key rate in quantum key distribution (QKD) protocols is a long standing challenge. Analytical methods are limited to a handful of protocols with highly symmetric measurement bases.
Mateus Araújo +4 more
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