DCISolver - Dynamic Control of Infeasibility Solver

DCI is a solver for equality-constrained nonlinear problems, i.e., optimization problems of the form

min f(x)     s.t.     c(x) = 0.

It uses other JuliaSmoothOptimizers packages for development. In particular, NLPModels.jl is used for defining the problem, and SolverCore for the output. It uses LDLFactorizations.jl by default to compute the factorization in the tangent step. Follow HSL.jl's MA57 installation for an alternative. The feasibility steps are factorization-free and use iterative methods from Krylov.jl

References

Bielschowsky, R. H., & Gomes, F. A. Dynamic control of infeasibility in equality constrained optimization. SIAM Journal on Optimization, 19(3), 1299-1325 (2008). 10.1007/s10589-020-00201-2

How to Cite

If you use DCISolver.jl in your work, please cite using the format given in CITATION.bib.

Installation

1. LDLFactorizations.jl is used by default. Follow HSL.jl's MA57 installation for an alternative.
2. pkg> add DCISolver

Example

using DCISolver, NLPModels

# Rosenbrock
nlp = ADNLPModel(x -> 100 * (x[2] - x[1]^2)^2 + (x[1] - 1)^2, [-1.2; 1.0])
stats = dci(nlp, nlp.meta.x0)

# Constrained
nlp = ADNLPModel(x -> 100 * (x[2] - x[1]^2)^2 + (x[1] - 1)^2, [-1.2; 1.0],
                 x->[x[1] * x[2] - 1], [0.0], [0.0])
stats = dci(nlp, nlp.meta.x0)