An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization
Open Access
- 29 November 2022
- journal article
- research article
- Published by Springer Nature in Mathematical Programming
- Vol. 201 (1-2) , 409-472
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- ARL DCIST CRA (W911NF-17-2-0181)
- NSF CAREER (“Certifiable Perception for Autonomous Cyber-Physical Systems”)
- Ministry of Education, Singapore (MOE-2019-T3-1-010)
This publication has 78 references indexed in Scilit:
- Software for weighted structured low-rank approximationJournal of Computational and Applied Mathematics, 2014
- Projection methods for conic feasibility problems: applications to polynomial sum-of-squares decompositionsOptimization Methods and Software, 2011
- The complexity of optimizing over a simplex, hypercube or sphere: a short surveyCentral European Journal of Operations Research, 2007
- Semidefinite representations for finite varietiesMathematical Programming, 2006
- GloptiPolyACM Transactions on Mathematical Software, 2003
- Semidefinite programming relaxations for semialgebraic problemsMathematical Programming, 2003
- A nonlinear programming algorithm for solving semidefinite programs via low-rank factorizationMathematical Programming, 2003
- Computing the nearest correlation matrix--a problem from financeIMA Journal of Numerical Analysis, 2002
- Benchmarking optimization software with performance profilesMathematical Programming, 2002
- Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programmingJournal of the ACM, 1995