2022年7月27日 –, JuMP
In this talk, we describe a Julia implementation of RipQP, a regularized interior-point method for convex quadratic optimization. RipQP is able to solve problems in several floating-point formats, and can also start in a lower precision as a form of warm-start. The algorithm uses sparse factorizations or Krylov methods from the Julia package Krylov.jl. We present an easy way to use RipQP to solve problems modeled with QuadraticModels.jl and LLSModels.jl.
Phd student at Polytechnique Montréal supervised by Dominique Orban. I am developing RipQP.jl, a package for solving convex quadratic problems and I also contribute to the organization JuliaSmoothOptimizers.