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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
doaj +7 more sources
Multireference Alignment using Semidefinite Programming [PDF]
Proceedings of the 5th conference on Innovations in theoretical computer science, 2013 The multireference alignment problem consists of estimating a signal from multiple noisy shifted observations. Inspired by existing Unique-Games approximation algorithms, we provide a semidefinite program (SDP) based relaxation which approximates the ...
Bandeira, Afonso S. +3 more
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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?
Elad, Noa +2 more
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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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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.
Yurtsever, Alp +4 more
openaire +3 more sources
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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Numerical algebraic geometry and semidefinite programming
Results in Applied Mathematics, 2021 Standard interior point methods in semidefinite programming can be viewed as tracking a solution path for a homotopy defined by a system of bilinear equations.
Jonathan D. Hauenstein +3 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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Exact Optimal Designs of Experiments for Factorial Models via Mixed-Integer Semidefinite Programming
Mathematics, 2023 The systematic design of exact optimal designs of experiments is typically challenging, as it results in nonconvex optimization problems. The literature on the computation of model-based exact optimal designs of experiments via mathematical programming ...
Belmiro P. M. Duarte
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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
core +5 more sources