• Main purpose of my talk is “an introduction to the recent development of SDP relaxation in connection with the classical Lagrangian relaxation”. • Although the title includes “polynomial optimization problems”, I will mainly talk about “quadratic optimization problems ” for simplicity of discussions. • But most of the discussions can be extended to “polynomial optimiza-tion problems”. • This material is available a
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynom...
International audienceThis is the first comprehensive introduction to the powerful moment approach f...
International audienceWe consider the general polynomial optimization problem $P: f^*=\min \{f(x)\,:...
International audienceWe consider the general polynomial optimization problem $P: f^*=\min \{f(x)\,:...
A polynomial SDP (semidefinite programs) minimizes a polynomial objective function over a feasible r...
n recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very e...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
Lagrangian relaxation is commonly used in combinatorial optimization to generate lower bounds for a ...
Abstract. We present the moment cone (MC) relaxation and a hierarchy of sparse Lagrangian-SDP relaxa...
The goal of this thesis is to study a special nonlinear programming, namely, polynomial optimization...
A hierarchy of semidefinite programming (SDP) relaxations approxi-mates the global optimum of polyno...
We study how to solve semidefinite programming (SDP) relaxations for large scale polynomial optimiza...
Semidefinite programming (SDP) is currently one of the most active areas of research in optimization...
We consider partial lagrangian relaxations of continuous quadratic formulations of the Quadratic Ass...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynom...
International audienceThis is the first comprehensive introduction to the powerful moment approach f...
International audienceWe consider the general polynomial optimization problem $P: f^*=\min \{f(x)\,:...
International audienceWe consider the general polynomial optimization problem $P: f^*=\min \{f(x)\,:...
A polynomial SDP (semidefinite programs) minimizes a polynomial objective function over a feasible r...
n recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very e...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
Lagrangian relaxation is commonly used in combinatorial optimization to generate lower bounds for a ...
Abstract. We present the moment cone (MC) relaxation and a hierarchy of sparse Lagrangian-SDP relaxa...
The goal of this thesis is to study a special nonlinear programming, namely, polynomial optimization...
A hierarchy of semidefinite programming (SDP) relaxations approxi-mates the global optimum of polyno...
We study how to solve semidefinite programming (SDP) relaxations for large scale polynomial optimiza...
Semidefinite programming (SDP) is currently one of the most active areas of research in optimization...
We consider partial lagrangian relaxations of continuous quadratic formulations of the Quadratic Ass...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynom...
International audienceThis is the first comprehensive introduction to the powerful moment approach f...