This paper presents new approximation bounds for trilinear and biquadratic optimization problems over nonconvex constraints. We first consider the partial semidefinite relaxation of the original problem, and show that there is a bounded approximation solution to it. This will be achieved by determining the diameters of certain convex bodies. We then show that there is also a bounded approximation solution to the original problem via extracting the approximation solution of its semidefinite relaxation. Under some conditions, the approximation bounds obtained in this paper improve those in the literature.Department of Applied Mathematic
We consider the non-convex quadratic maximization problem subject to the l1 unit ball constraint. Th...
We consider the non-convex quadratic maximization problem subject to the l1 unit ball constraint. Th...
We prove some non-approximability results for restrictions of basic combinatorial optimization probl...
This paper studies the relationship between the so-called bi-quadratic optimization problem and its ...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
Bi-quadratic optimization, Semidefinite programming relaxation, Approximation solution, Probabilisti...
AbstractWe consider binary convex quadratic optimization problems, particularly those arising from r...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
This paper studies the so-called biquadratic optimization over unit spheres minx∈Rn,y∈Rm 1≤i,k≤n, 1≤...
AbstractThere have been several attempts to develop a unified approach to the characterization of so...
In this paper, we consider a class of quadratic maximization problems. One important instance in tha...
SoumisNational audienceThis paper presents new semidefinite programming bounds for 0-1 quadratic pro...
SoumisNational audienceThis paper presents new semidefinite programming bounds for 0-1 quadratic pro...
In this paper, we consider the NP-hard problem of finding global minimum of quadratically constraine...
In this article, we use abstract convexity results to study augmented dual problems for (nonconvex) ...
We consider the non-convex quadratic maximization problem subject to the l1 unit ball constraint. Th...
We consider the non-convex quadratic maximization problem subject to the l1 unit ball constraint. Th...
We prove some non-approximability results for restrictions of basic combinatorial optimization probl...
This paper studies the relationship between the so-called bi-quadratic optimization problem and its ...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
Bi-quadratic optimization, Semidefinite programming relaxation, Approximation solution, Probabilisti...
AbstractWe consider binary convex quadratic optimization problems, particularly those arising from r...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
This paper studies the so-called biquadratic optimization over unit spheres minx∈Rn,y∈Rm 1≤i,k≤n, 1≤...
AbstractThere have been several attempts to develop a unified approach to the characterization of so...
In this paper, we consider a class of quadratic maximization problems. One important instance in tha...
SoumisNational audienceThis paper presents new semidefinite programming bounds for 0-1 quadratic pro...
SoumisNational audienceThis paper presents new semidefinite programming bounds for 0-1 quadratic pro...
In this paper, we consider the NP-hard problem of finding global minimum of quadratically constraine...
In this article, we use abstract convexity results to study augmented dual problems for (nonconvex) ...
We consider the non-convex quadratic maximization problem subject to the l1 unit ball constraint. Th...
We consider the non-convex quadratic maximization problem subject to the l1 unit ball constraint. Th...
We prove some non-approximability results for restrictions of basic combinatorial optimization probl...