Fast Randomized Entropically Regularized Semidefinite Programming
Abstract
Abstract. We develop a practical approach to semidefinite programming (SDP) that includes the von Neumann entropy, or an appropriate variant, as a regularization term. In particular we solve the dual of the regularized program, demonstrating how a carefully chosen randomized trace estimator can be used to estimate dual gradients effectively. We also introduce specialized optimization approaches for common SDP, specifically SDP with diagonal constraint and the problem of the determining the spectral projector onto the span of extremal eigenvectors. We validate our approach on such problems with applications to combinatorial optimization and spectral embedding.
Fields of Study & Subjects
Advanced Optimization Algorithms Research
Markov Chains and Monte Carlo Methods
Machine Learning and Algorithms
Publication details
- Journal / source
- SIAM Journal on Scientific Computing
- Volume
- 48
- Issue
- 4
- Pages
- A2085–A2104
- Publication date
- 30 July 2026
- Language
- EN