Name
Abstract | ||
|---|---|---|
A polynomial optimization problem whose objective function is represented as a sum of positive and even powers of polynomials, called a polynomial least squares problem, is considered. Methods to transform a polynomial least square problem to polynomial semidefinite programs to reduce degrees of the polynomials are discussed. Computational efficiency of solving the original polynomial least squares problem and the transformed polynomial semidefinite programs is compared. Numerical results on selected polynomial least square problems show better computational performance of a transformed polynomial semidefinite program, especially when degrees of the polynomials are larger. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10898-009-9405-3 | J. Global Optimization |
Keywords | Field | DocType |
Nonconvex optimization problems,Polynomial least squares problems,Polynomial semidefinite programs,Polynomial second-order cone programs,Sparsity | Mathematical optimization,Stable polynomial,Polynomial matrix,Mathematical analysis,Monic polynomial,Reciprocal polynomial,Matrix polynomial,Symmetric polynomial,Mathematics,Factorization of polynomials,Polynomial least squares | Journal |
Volume | Issue | ISSN |
46 | 1 | 0925-5001 |
Citations | PageRank | References |
2 | 0.41 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunyoung Kim | 1 | 461 | 38.82 |
| Masakazu Kojima | 2 | 1603 | 222.51 |