Large-scale semidefinite programming via a saddle point mirror-prox algorithm

DOI10.1007/s10107-006-0031-2zbMath1148.90009OpenAlexW2113717009WikidataQ57392914 ScholiaQ57392914MaRDI QIDQ868467

Zhaosong Lu, Renato D. C. Monteiro, Arkadi Nemirovski

Publication date: 5 March 2007

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10107-006-0031-2

zbMATH Keywords

Semidefinite programmingsaddle point problemmirror prox method

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Numerical optimization and variational techniques (65K10) Interior-point methods (90C51)

Related Items

Sparse non Gaussian component analysis by semidefinite programming, Primal-dual first-order methods with \({\mathcal {O}(1/\varepsilon)}\) iteration-complexity for cone programming, An optimal method for stochastic composite optimization, Accelerated first-order methods for hyperbolic programming, Exploiting equalities in polynomial programming, Solving variational inequalities with Stochastic Mirror-Prox algorithm, A novel approach for solving semidefinite programs

Uses Software

• SDPLR

Cites Work

• Smooth minimization of non-smooth functions
• Positive definite completions of partial Hermitian matrices
• A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
• Solving a class of semidefinite programs via nonlinear programming
• Exploiting Sparsity in Semidefinite Programming via Matrix Completion I: General Framework
• A Spectral Bundle Method for Semidefinite Programming
• Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems