# Branch Information

This is a branch of Complementarity. The goal of this branch is to update Complementarity to work with the newest version of JuMP. Currently the only broken test is MCEP.

# Complementarity.jl

This package provides modeling language for (1) mixed complementarity problems (MCP) and (2) mathematical programs with equilibrium problems (MPEC).

**NOTE** `@complmentarity`

for MCP and `@complements`

for MPEC.

## Mixed Complementarity Problems (MCP)

**NOTE:** The PATHSolver.jl has been completely rewritten between `v0.6.2`

and `v1.1.0`

. Now PATHSolver.jl provides both an interface to the PATH solver and an integration to JuMP, but only limited to *linear* problems at this moment. For *nonlinear* problems, you still need to use Complementarity.jl, which now also uses the new PATHSolver.jl as its solver. Most parts of Complementarity.jl remain the same, except how the solver options are passed.

- This package provides a modeling and computational interface for solving Mixed Complementarity Problems (MCP): modeling by JuMP.jl and computing by PATHSolver.jl and NLsolve.jl. See the documentation.

```
F(x) ⟂ lb ≤ x ≤ ub
```

A very simple example:

```
(x+2) x = 0, x ≥ 0, x+2 ≥ 0
```

using Complementarity, JuMP
m = MCPModel()
@variable(m, x >= 0)
@mapping(m, F, x+2)
@complementarity(m, F, x)
status = solveMCP(m)
@show result_value(x)

## Mathematical Programs with Equilibrium Constraints (MPEC)

**NOTE:** For solving MPEC, JuMP.jl `v0.21`

has started supporting complementarity constraints. At this moment, GAMS.jl and KNITRO support complementarity constraints.

- For solving mathematical programs with equilibrium constraints (MPEC), this package provides an extension to JuMP.jl by providing a macro that accepts complementarity conditions as constraints. Then it reformulates the complementarity conditions as a set of equality and inequality constraints so that a nonlinear optimization solver such as Ipopt.jl can solve the problem. See the documentation.

```
min f(x)
s.t. g(x) ≤ 0
F(x) ⟂ lb ≤ x ≤ ub
```

A very simple example:

```
min x^3
s.t. (x+2) x = 0, x ≥ 0, x+2 ≥ 0
```

using JuMP, Ipopt, Complementarity
m = Model(Ipopt.Optimizer)
@variable(m, x>=0)
@NLobjective(m, Min, x^3)
@complements(m, 0 <= x+2, x >= 0)
solve(m)
@show getvalue(x)

# Installation

Pkg.add("Complementarity")

This will also install a few other packages.