Reference

IpoptSolver(nlp; kwargs...,)

Returns an IpoptSolver structure to solve the problem nlp with ipopt.

solver = reset!(solver::IpoptSolver, nlp::AbstractNLPModel)

Reset the solver with the new model nlp.

If nlp has different bounds on the variables/constraints or a different number of nonzeros elements in the Jacobian/Hessian, then you need to create a new IpoptSolver.

output = ipopt(nlp; kwargs...)

Solves the NLPModel problem nlp using IpOpt.

For advanced usage, first define a IpoptSolver to preallocate the memory used in the algorithm, and then call solve!: solver = IpoptSolver(nlp) solve!(solver, nlp; kwargs...) solve!(solver, nlp, stats; kwargs...)

Optional keyword arguments

• x0: a vector of size nlp.meta.nvar to specify an initial primal guess
• y0: a vector of size nlp.meta.ncon to specify an initial dual guess for the general constraints
• zL: a vector of size nlp.meta.nvar to specify initial multipliers for the lower bound constraints
• zU: a vector of size nlp.meta.nvar to specify initial multipliers for the upper bound constraints

All other keyword arguments will be passed to Ipopt as an option. See https://coin-or.github.io/Ipopt/OPTIONS.html for the list of options accepted.

Output

The returned value is a GenericExecutionStats, see SolverCore.jl.

Examples

using NLPModelsIpopt, ADNLPModels
nlp = ADNLPModel(x -> sum(x.^2), ones(3));
stats = ipopt(nlp, print_level = 0)

using NLPModelsIpopt, ADNLPModels
nlp = ADNLPModel(x -> sum(x.^2), ones(3));
solver = IpoptSolver(nlp);
stats = solve!(solver, nlp, print_level = 0)

set_callbacks(nlp::AbstractNLPModel)

Return the set of functions needed to instantiate an IpoptProblem.