Add to ORKGWe show that optimizing over non-symmetric matrices is not polynomial time solvable, unless P=NP. This is in contrast to the symmetric case for which several polynomial time algorithms are known
8 pages, 3 tablesInternational audienceIn this article, we show that each semidefinite relaxation of...
We prove complexity bounds for Schmüdgen's Positivstellensatz and investigate the recently popular a...
A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynom...
We show that optimizing over non-symmetrical matrices is not polynomial solvable unless P=NP. This i...
In semidefinite programming one minimizes a linear function subject to the constraint that an affine...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very ...
textabstractThis book presents recent results on positivity and optimization of polynomials in non-c...
In this paper, we consider the non-symmetric positive semidefinite Procrustes (NSPSDP) problem: Give...
This book presents recent results on positivity and optimization of polynomials in non-commuting var...
This paper is concerned with the study of an arbitrary polynomial optimization via a convex relaxati...
We consider the general feasibility problem for semidefinite programming: Determine whether a given ...
In Semidefinite programming one minimizes a linear function sub-ject to the constraint that an affin...
In this paper we investigate matrix inequalities which hold irrespective of the size of the matrices...
Many problems of systems control theory boil down to solving polynomial equations, polynomial inequa...
We use techniques from (tracial noncommutative) polynomial optimization to formulate hierarchies of ...
8 pages, 3 tablesInternational audienceIn this article, we show that each semidefinite relaxation of...
We prove complexity bounds for Schmüdgen's Positivstellensatz and investigate the recently popular a...
A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynom...
We show that optimizing over non-symmetrical matrices is not polynomial solvable unless P=NP. This i...
In semidefinite programming one minimizes a linear function subject to the constraint that an affine...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very ...
textabstractThis book presents recent results on positivity and optimization of polynomials in non-c...
In this paper, we consider the non-symmetric positive semidefinite Procrustes (NSPSDP) problem: Give...
This book presents recent results on positivity and optimization of polynomials in non-commuting var...
This paper is concerned with the study of an arbitrary polynomial optimization via a convex relaxati...
We consider the general feasibility problem for semidefinite programming: Determine whether a given ...
In Semidefinite programming one minimizes a linear function sub-ject to the constraint that an affin...
In this paper we investigate matrix inequalities which hold irrespective of the size of the matrices...
Many problems of systems control theory boil down to solving polynomial equations, polynomial inequa...
We use techniques from (tracial noncommutative) polynomial optimization to formulate hierarchies of ...
8 pages, 3 tablesInternational audienceIn this article, we show that each semidefinite relaxation of...
We prove complexity bounds for Schmüdgen's Positivstellensatz and investigate the recently popular a...
A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynom...