12 citations · 40 across the 9 of their papers we have counts for
6 papers · 2 filters
Exploiting constant trace property in large-scale polynomial optimization
Ngoc Hoang Anh Mai, Jean-Bernard Lasserre, Victor Magron +1
We prove that every semidefinite moment relaxation of a polynomial optimization problem (POP) with a ball constraint can be reformulated as a semidefinite program involving a matri…
SONC Optimization and Exact Nonnegativity Certificates via Second-Order Cone Programming
Victor Magron, Jie Wang
The second-order cone (SOC) is a class of simple convex cones and optimizing over them can be done more efficiently than with semidefinite programming. It is interesting both in th…
Exploiting term sparsity in Noncommutative Polynomial Optimization
Jie Wang, Victor Magron
We provide a new hierarchy of semidefinite programming relaxations, called NCTSSOS, to solve large-scale sparse noncommutative polynomial optimization problems. This hierarchy feat…
SparseJSR: A Fast Algorithm to Compute Joint Spectral Radius via Sparse SOS Decompositions
Jie Wang, Martina Maggio, Victor Magron
This paper focuses on the computation of joint spectral radii (JSR), when the involved matrices are sparse. We provide a sparse variant of the procedure proposed by Parrilo and Jad…
CS-TSSOS: Correlative and term sparsity for large-scale polynomial optimization
Jie Wang, Victor Magron, Jean B. Lasserre +1
This work proposes a new moment-SOS hierarchy, called CS-TSSOS, for solving large-scale sparse polynomial optimization problems. Its novelty is to exploit simultaneously correlativ…
Chordal-TSSOS: a moment-SOS hierarchy that exploits term sparsity with chordal extension
Jie Wang, Victor Magron, Jean-Bernard Lasserre
This work is a follow-up and a complement to arXiv:1912.08899 [math.OC] for solving polynomial optimization problems (POPs). The chordal-TSSOS hierarchy that we propose is a new sp…