Abstract
We discuss fast implementations of primal-dual interior-point methods for semidefinite programs derived from the Kalman-Yakubovich-Popov lemma, a class of problems that are widely encountered in control and signal processing applications. By exploiting problem structure we achieve a reduction of the complexity by several orders of magnitude compared to general-purpose semidefinite programming solvers.
Keywords
Affiliated Institutions
Related Publications
A Direct Formulation for Sparse PCA Using Semidefinite Programming
Given a covariance matrix, we consider the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the number o...
SDPT3 — A Matlab software package for semidefinite programming, Version 1.3
This software package is a MATLAB implementation of infeasible path-following algorithms for solving standard semidefinite programs (SDP). Mehrotra-type predictor-corrector vari...
Determinant Maximization with Linear Matrix Inequality Constraints
The problem of maximizing the determinant of a matrix subject to linear matrix inequalities (LMIs) arises in many fields, including computational geometry, statistics, system id...
Publication Info
- Year
- 2004
- Type
- article
- Pages
- 4658-4663
- Citations
- 11
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1109/cdc.2003.1272303