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Full Description
Finding low-rank solutions of semidefinite programs is important in many applications. For example, semidefinite programs that arise as relaxations of polynomial optimization problems are exact relaxations when the semidefinite program has a rank-1 solution. Unfortunately, computing a minimum-rank solution of a semidefinite program is an NP-hard problem.
This monograph reviews the theory of low-rank semidefinite programming, presenting theorems that guarantee the existence of a low-rank solution, heuristics for computing low-rank solutions, and algorithms for finding low-rank approximate solutions. It then presents applications of the theory to trust-region problems and signal processing.
Contents
1: Introduction
PART I THEORY 2: Exact Solutions and Theorems about Rank
3: Heuristics and Approximate Solutions
PART II APPLICATIONS
4: Trust-Region Problems
5: QCQPs with Complex Variables
Appendices A, B, C
References