 |
Free and accessible knowledge |
Conference papers
Semidefinite programming relaxations through quadratic reformulation for box-constrained polynomial optimization problems
Abstract : In this paper we introduce new semidefinite programming relaxations to box-constrained polynomial optimization programs (P). For this, we first reformu-late (P) into a quadratic program. More precisely, we recursively reduce the degree of (P) to two by substituting the product of two variables by a new one. We obtain a quadratically constrained quadratic program. We build a first immediate SDP relaxation in the dimension of the total number of variables. We then strengthen the SDP relaxation by use of valid constraints that follow from the quadratization. We finally show the tightness of our relaxations through several experiments on box polynomial instances.
Cited literature [20 references]
https://hal.archives-ouvertes.fr/hal-02455410
Contributor : Amélie Lambert Connect in order to contact the contributor
Submitted on : Sunday, January 26, 2020 - 10:29:06 AM
Last modification on : Wednesday, May 11, 2022 - 12:06:06 PM
Long-term archiving on: : Monday, April 27, 2020 - 4:19:07 PM
Contributor : Amélie Lambert Connect in order to contact the contributor
Submitted on : Sunday, January 26, 2020 - 10:29:06 AM
Last modification on : Wednesday, May 11, 2022 - 12:06:06 PM
Long-term archiving on: : Monday, April 27, 2020 - 4:19:07 PM
File
BLL2019_codit.pdf
Files produced by the author(s)