Using optimization to obtain a width-independent, parallel, simpler, and faster positive SDP solver
We study the design of polylogarithmic depth algorithms for approximately solving packing
and covering semidefinite programs (or positive SDPs for short). This is a natural SDP …
and covering semidefinite programs (or positive SDPs for short). This is a natural SDP …
Faster and simpler width-independent parallel algorithms for positive semidefinite programming
R Peng, K Tangwongsan - Proceedings of the twenty-fourth annual ACM …, 2012 - dl.acm.org
This paper studies the problem of finding a (1+ ε)-approximate solution to positive
semidefinite programs. These are semidefinite programs in which all matrices in the …
semidefinite programs. These are semidefinite programs in which all matrices in the …
Approximating semidefinite programs in sublinear time
In recent years semidefinite optimization has become a tool of major importance in various
optimization and machine learning problems. In many of these problems the amount of data …
optimization and machine learning problems. In many of these problems the amount of data …
Decremental -Approximate Maximum Eigenvector: Dynamic Power Method
D Adil, T Saranurak - arXiv preprint arXiv:2402.17929, 2024 - arxiv.org
We present a dynamic algorithm for maintaining $(1+\epsilon) $-approximate maximum
eigenvector and eigenvalue of a positive semi-definite matrix $ A $ undergoing\emph …
eigenvector and eigenvalue of a positive semi-definite matrix $ A $ undergoing\emph …
A primal barrier function Phase I algorithm for nonsymmetric conic optimization problems
Y Matsukawa, A Yoshise - Japan journal of industrial and applied …, 2012 - Springer
We call a positive semidefinite matrix whose elements are nonnegative a doubly
nonnegative matrix, and the set of those matrices the doubly nonnegative cone (DNN cone) …
nonnegative matrix, and the set of those matrices the doubly nonnegative cone (DNN cone) …
A conditional gradient homotopy method with applications to Semidefinite Programming
We propose a new homotopy-based conditional gradient method for solving convex
optimization problems with a large number of simple conic constraints. Instances of this …
optimization problems with a large number of simple conic constraints. Instances of this …
Scalable constrained optimization
ML Vladarean - 2024 - infoscience.epfl.ch
Modern optimization is tasked with handling applications of increasingly large scale, chiefly
due to the massive amounts of widely available data and the ever-growing reach of Machine …
due to the massive amounts of widely available data and the ever-growing reach of Machine …
Finding Sparse Solutions for Packing and Covering Semidefinite Programs
K Elbassioni, K Makino - arXiv preprint arXiv:1809.09698, 2018 - arxiv.org
Packing and covering semidefinite programs (SDPs) appear in natural relaxations of many
combinatorial optimization problems as well as a number of other applications. Recently …
combinatorial optimization problems as well as a number of other applications. Recently …
Finding sparse solutions for packing and covering semidefinite programs
K Elbassioni, K Makino, W Najy - SIAM Journal on Optimization, 2022 - SIAM
Packing and covering semidefinite programs (SDPs) appear in natural relaxations of many
combinatorial optimization problems as well as a number of other applications. Recently …
combinatorial optimization problems as well as a number of other applications. Recently …
[PDF][PDF] Oracle-based primal-dual algorithms for packing and covering semidefinite programs
K Elbassioni, K Makino - … : Algorithmic Revolution in the Big Data …, 2022 - library.oapen.org
Packing and covering semidefinite programs (SDPs) appear in natural relaxations of many
combinatorial optimization problems as well as a number of other applications. Recently …
combinatorial optimization problems as well as a number of other applications. Recently …