Matrix Sum-of-Squares Relaxations for Robust Semi-Definite Programs

By Carsten W. Scherer, Camile W. J. Hol. Mathematical programming, 2006

374 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma (2020, methodology): “In the special case that variables are bounded, an S-lemma for matrix variables can also be derived by combining the so-called full block S-procedure [47]–[50] with results from the literature on LMI relaxations [51]–[53].”
Parameter-Dependent LMIs in Robust Analysis: Characterization of Homogeneous Polynomially Parameter-Dependent Solutions Via LMI Relaxations (2007, methodology): “Table I shows a numerical comparison with the methods from [13], [14], and [20] and the results presented in this note.”
LMI Techniques for Optimization Over Polynomials in Control: A Survey (2010, methodology): “The following result investigates positivity of matrix polynomials on compact semi-algebraic sets [25].”