Add to ORKGI will present a deterministic polynomial time c^n approximation algorithm for the permanent of positive semidefinite matrices where c ~ 4.84. This is through a natural convex programming relaxation and proving a PSD variation of the Van der Waerden's theorem. Joint work with Nima Anari, Shayan Oveis Gharan, and Leonid Gurvitz.Non UBCUnreviewedAuthor affiliation: Stanford UniversityFacult
We consider the general feasibility problem for semidefinite programming: Determine whether a given ...
For arbitrary real matrices F and G, the positive semi-definite Procrustes problem is minimization o...
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...
I will present a deterministic polynomial time c^n approximation algorithm for the permanent of posi...
We present real, complex, and quaternionic versions of a simple randomized polynomial time algorithm...
We present real, complex, and quaternionic versions of a simple randomized polynomial time algorithm...
Abstract. A new approximation algorithm for the permanent of an n × n 0,1-matrix is presented. The a...
This thesis presents new theoretical results and algorithms for two matrix problems with positive se...
This paper studies the problem of finding a (1 + ε)-approximation to positive semidefinite programs....
Abstract. Indefinite approximations of positive semidefinite matrices arise in many data anal-ysis a...
Abstract Despite its apparent similarity to the (easily-computable) determinant, it is believed that...
In this work, we consider the problem of learning a positive semidefinite matrix. The critical issue...
Indefinite approximations of positive semidefinite matrices arise in many data analysis applications...
Abstract. Indefinite estimates of positive semidefinite matrices arise in many data analysis ap-plic...
We introduce a new method to construct approximation algorithms for combinatorial optimization probl...
We consider the general feasibility problem for semidefinite programming: Determine whether a given ...
For arbitrary real matrices F and G, the positive semi-definite Procrustes problem is minimization o...
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...
I will present a deterministic polynomial time c^n approximation algorithm for the permanent of posi...
We present real, complex, and quaternionic versions of a simple randomized polynomial time algorithm...
We present real, complex, and quaternionic versions of a simple randomized polynomial time algorithm...
Abstract. A new approximation algorithm for the permanent of an n × n 0,1-matrix is presented. The a...
This thesis presents new theoretical results and algorithms for two matrix problems with positive se...
This paper studies the problem of finding a (1 + ε)-approximation to positive semidefinite programs....
Abstract. Indefinite approximations of positive semidefinite matrices arise in many data anal-ysis a...
Abstract Despite its apparent similarity to the (easily-computable) determinant, it is believed that...
In this work, we consider the problem of learning a positive semidefinite matrix. The critical issue...
Indefinite approximations of positive semidefinite matrices arise in many data analysis applications...
Abstract. Indefinite estimates of positive semidefinite matrices arise in many data analysis ap-plic...
We introduce a new method to construct approximation algorithms for combinatorial optimization probl...
We consider the general feasibility problem for semidefinite programming: Determine whether a given ...
For arbitrary real matrices F and G, the positive semi-definite Procrustes problem is minimization o...
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...