Worst-case results for positive semidefinite rank
نویسندگان
چکیده
This paper presents various worst-case results on the positive semidefinite (psd) rank of a nonnegative matrix, primarily in the context of polytopes. We prove that the psd rank of a generic n-dimensional polytope with v vertices is at least (nv) 1 4 improving on previous lower bounds. For polygons with v vertices, we show that psd rank cannot exceed 4 dv/6e which in turn shows that the psd rank of a p × q matrix of rank three is at most 4 dmin{p, q}/6e. In general, a nonnegative matrix of rank (k+1 2 ) has psd rank at least k and we pose the problem of deciding whether the psd rank is exactly k. Using geometry and bounds on quantifier elimination, we show that this decision can be made in polynomial time when k is fixed.
منابع مشابه
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
Semidefinite programs (SDP) are important in learning and combinatorial optimization with numerous applica-tions. In pursuit of low-rank solutions and low complexity algorithms, we consider the Burer–Monteiro factorizationapproach for solving SDPs. We show that all approximate local optima are global optima for the penalty formulationof appropriately rank-constrained SDPs as long as...
متن کاملA path following interior-point algorithm for semidefinite optimization problem based on new kernel function
In this paper, we deal to obtain some new complexity results for solving semidefinite optimization (SDO) problem by interior-point methods (IPMs). We define a new proximity function for the SDO by a new kernel function. Furthermore we formulate an algorithm for a primal dual interior-point method (IPM) for the SDO by using the proximity function and give its complexity analysis, and then we sho...
متن کاملSemidefinite Programming Reformulation of Completely Positive Programs: Range Estimation and Best-Worst Choice Modeling
We show that the worst case moment bound on the expected optimal value of a mixed integer linear program with a random objective c is closely related to the complexity of characterizing the convex hull of the points { ( 1 x )( 1 x )′ | x ∈ X} where X is the feasible region. In fact, we can replace the completely positive programming characterization of the moment bound on X , with an associated...
متن کاملAn Interior Point Algorithm for Solving Convex Quadratic Semidefinite Optimization Problems Using a New Kernel Function
In this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual Interior Point Method (IPM) based on a new kernel function with a trigonometric barrier term. Iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. Although our proposed kernel function is neither a Self-Regular (SR) fun...
متن کاملPerformance Analysis of Quasi Integer Least Squares Solvers Based on Semidefinite Relaxation∗
We consider a random Integer Least Squares (ILS) problem. This NP-hard problem naturally arises in digital communications as the maximum-likelihood detection problem. We analyze two probabilistic quasi-ILS algorithms based on semidefinite relaxations: the SDR algorithm for binary variables and the PSK algorithm for constant modulus variables. Both algorithms are capable of delivering a near-opt...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Math. Program.
دوره 153 شماره
صفحات -
تاریخ انتشار 2015