12 citations · 12 across the 1 of their papers we have counts for
7 papers
TSSOS: a Julia library to exploit sparsity for large-scale polynomial optimization
Victor Magron, Jie Wang
The Julia library TSSOS aims at helping polynomial optimizers to solve large-scale problems with sparse input data. The underlying algorithmic framework is based on exploiting corr…
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…
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…
TSSOS: A Moment-SOS hierarchy that exploits term sparsity
Jie Wang, Victor Magron, Jean-Bernard Lasserre
This paper is concerned with polynomial optimization problems. We show how to exploit term (or monomial) sparsity of the input polynomials to obtain a new converging hierarchy of s…
A second order cone characterization for sums of nonnegative circuits
Jie Wang, Victor Magron
The second-order cone 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 theory a…