An Inner Convex Approximation Algorithm for BMI Optimization and Applications in Control
read the original abstract
In this work, we propose a new local optimization method to solve a class of nonconvex semidefinite programming (SDP) problems. The basic idea is to approximate the feasible set of the nonconvex SDP problem by inner positive semidefinite convex approximations via a parameterization technique. This leads to an iterative procedure to search a local optimum of the nonconvex problem. The convergence of the algorithm is analyzed under mild assumptions. Applications in static output feedback control are benchmarked and numerical tests are implemented based on the data from the COMPLeib library.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Robust Distributed Sub-Optimal Coordination of Linear Agents with Uncertain Input Nonlinearities
A novel robust control protocol ensures linear agents with sector-bounded input nonlinearities converge to a neighborhood of the global optimizer, with solvability conditions given as matrix inequalities.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.