DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility

Abstract

DCISolver.jl is a new Julia (Bezanson et al., 2017) implementation of the Dynamic Control of Infeasibility method (DCI), introduced by Bielschowsky & Gomes ( 2008), for solving the equality-constrained nonlinear optimization problem minimizewhere f : R n → R and h : R n → R m are twice continuously differentiable. DCI is an iterative method that aims to compute a local minimum of (1) using first and second-order derivatives.Our initial motivation for developing DCISolver.jl is to solve PDE-constrained optimization problems, many of which have equality constraints only.

Enlsip.jl: A Julia optimization package to solve constrained nonlinear least-squares problems

“…For unconstrained and bound-constrained problems, these include NL2sol.jl (Dennis Jr et al, 1981), LsqFit.jl and LeastSquaresOptim.jl. Also available are least-squares variants of the TRUNK and TRON (Lin & Moré, 1999) solvers from JSOSolvers.jl (Migot et al, 2023). The CaNNOLeS.jl package handles the equality-constrained case.…”

PDENLPModels.jl: An NLPModel API for Optimization Problems with PDE-Constraints

“…PDENLPModels.jl provides all the extra facilities for users and solvers to interact with a PDE-constrained optimization problem as they would with a JuMP model, an AMPL model, or any other model that complies with the NLPModels API. As such, PDENLPModels.jl offers an interface between generic PDE-constrained optimization problems and cutting-edge optimization solvers such as Artelys Knitro (Byrd et al, 2006) via NLPModelsKnitro.jl (Orban et al, 2020e), Ipopt (Wächter & Biegler, 2006) via NLPModelsIpopt.jl (Orban et al, 2020c), DCISolver.jl (Migot et al, 2022), Percival.jl (dos Santos & Siqueira, 2020), and any solver accepting an AbstractNLPModel as input, see JuliaSmoothOptimizers (JSO) (Migot et al, 2021).…”

“…Migot et al (2022). PDENLPModels.jl: An NLPModel API for Optimization Problems with PDE-Constraints.…”