This paper deals with convex relaxations for quadratic distance problems, a class of optimization problems relevant to several important topics in the analysis and synthesis of robust control systems. Some classes of convex relaxations are investigated using the sum of squares paradigm for the representation of positive polynomials. The main contribution is to show that two different relaxations, based respectively on the Positivstellensatz and on properties of homogeneous polynomial forms, are equivalent. Relationships among the considered relaxations are discussed and numerical comparisons are presented, highlighting their degree of conservatism
Abstract — This paper gives a sufficient condition for a robust control problem in terms of minimax ...
A hierarchy of convex relaxations for semialgebraic problems is introduced. For questions reducible ...
A polynomial SDP (semidefinite programs) minimizes a polynomial objective function over a feasible r...
This paper deals with convex relaxations for quadratic distance problems, a class of optimization pr...
Convex relaxations of nonconvex problems are a powerful tool for the analysis and design of control ...
The objective of the paper is to investigate relationships among different convex relaxations for qu...
It is the intention of the authors of this paper to provide the reader with a general view of convex...
Several problems relevant to robust analysis and design of control systems can be formulated in term...
We present an extension of the scalar polynomial optimization by sum-of squares de-compositions [5] ...
Dedicated to Boris Mordukhovich on the occasion of his 65th Birthday In this paper we present a new ...
In this paper, under a suitable regularity condition, we establish a broad class of conic convex pol...
The stability and performance analysis of dynamic systems affected by structured uncertainties usua...
This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class...
Convex relaxations are a central tool in modern algorithm design, but mathematically analyzingthe pe...
This book presents a number of techniques for robustness analysis of uncertain systems. The theoreti...
Abstract — This paper gives a sufficient condition for a robust control problem in terms of minimax ...
A hierarchy of convex relaxations for semialgebraic problems is introduced. For questions reducible ...
A polynomial SDP (semidefinite programs) minimizes a polynomial objective function over a feasible r...
This paper deals with convex relaxations for quadratic distance problems, a class of optimization pr...
Convex relaxations of nonconvex problems are a powerful tool for the analysis and design of control ...
The objective of the paper is to investigate relationships among different convex relaxations for qu...
It is the intention of the authors of this paper to provide the reader with a general view of convex...
Several problems relevant to robust analysis and design of control systems can be formulated in term...
We present an extension of the scalar polynomial optimization by sum-of squares de-compositions [5] ...
Dedicated to Boris Mordukhovich on the occasion of his 65th Birthday In this paper we present a new ...
In this paper, under a suitable regularity condition, we establish a broad class of conic convex pol...
The stability and performance analysis of dynamic systems affected by structured uncertainties usua...
This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class...
Convex relaxations are a central tool in modern algorithm design, but mathematically analyzingthe pe...
This book presents a number of techniques for robustness analysis of uncertain systems. The theoreti...
Abstract — This paper gives a sufficient condition for a robust control problem in terms of minimax ...
A hierarchy of convex relaxations for semialgebraic problems is introduced. For questions reducible ...
A polynomial SDP (semidefinite programs) minimizes a polynomial objective function over a feasible r...