Add to ORKGSemidefinite programming (SDP) has important applications in optimization problems that involve moment cones or, by duality, cones of nonnegative polynomials. Examples can be found in statistics, signal processing, control, and non-convex polynomial optimization. The talk will give an introduction to the connections between moment theory and SDP, and discuss SDP algorithms in this context. The focus will be on applications in experiment design.Non UBCUnreviewedAuthor affiliation: UCLAFacult
Abstract. The SDPA (SemiDefinite Programming Algorithm) is a software package for solv-ing semidefin...
We observe that in a simple one-dimensional polynomial optimization problem (POP), the `optimal' val...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
Semidefinite programming (SDP) has important applications in optimization problems that involve mome...
Semidefinite programming (SDP) is an extension of linear programming, with vector variables replaced...
Semidefinite programming (SDP) may be seen as a generalization of linear programming (LP). In partic...
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking po...
Many important applications in global optimization, algebra, probability and statistics, applied mat...
Semidefinite Programming (SDP) is a class of convex optimization problems with a linear objective fu...
In semidefinite programming one minimizes a linear function subject to the constraint that an affine...
It is well known that the duality theory for linear programming (LP) is powerful and elegant and lie...
The semidefinite programming has various important applications to combinato-rial optimization. This...
• Main purpose of my talk is “an introduction to the recent development of SDP relaxation in connect...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very ...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
Abstract. The SDPA (SemiDefinite Programming Algorithm) is a software package for solv-ing semidefin...
We observe that in a simple one-dimensional polynomial optimization problem (POP), the `optimal' val...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
Semidefinite programming (SDP) has important applications in optimization problems that involve mome...
Semidefinite programming (SDP) is an extension of linear programming, with vector variables replaced...
Semidefinite programming (SDP) may be seen as a generalization of linear programming (LP). In partic...
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking po...
Many important applications in global optimization, algebra, probability and statistics, applied mat...
Semidefinite Programming (SDP) is a class of convex optimization problems with a linear objective fu...
In semidefinite programming one minimizes a linear function subject to the constraint that an affine...
It is well known that the duality theory for linear programming (LP) is powerful and elegant and lie...
The semidefinite programming has various important applications to combinato-rial optimization. This...
• Main purpose of my talk is “an introduction to the recent development of SDP relaxation in connect...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of very ...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...
Abstract. The SDPA (SemiDefinite Programming Algorithm) is a software package for solv-ing semidefin...
We observe that in a simple one-dimensional polynomial optimization problem (POP), the `optimal' val...
In recent years, the semidefinite relaxation (SDR) technique has been at the center of some of the v...