```@meta CurrentModule = DataEnvelopmentAnalysis DocTestSetup = quote using DataEnvelopmentAnalysis end ``` # Configuring the optimizer DataEnvelopmentAnalysis.jl will use a default optimizer/solver for each DEA model, as shown in the next table. | Function | Specific Options | Problem type | Default Optimizer | | ---------------|--------:|------------:|------------------:| | `dea` | | LP | GLPK | | `deabigdata` | | LP | GLPK | | `deaddf` | | LP | GLPK | | `deaadd` | | LP | GLPK | | `deagdf` | | NLP | Ipopt | | `dearussell` | `:Input` or `:Output` | LP | GLPK | | `dearussell` | `:Graph` | NLP | Ipopt | | `deaerg` | | LP | GLPK | | `deamddf` | | LP | GLPK | | `deaholder` | `l = 1` | LP | GLPK | | `deaholder` | `l = 2` | QP | | | `deaholder` |`l = Inf`| LP | GLPK | | `dearddf` | `:ERG` | LP | GLPK | | `dearddf` | `:MDDF` | LP | GLPK | | `deacost` | | LP | GLPK | | `dearevenue` | | LP | GLPK | | `deaprofit` | | LP | GLPK | | `deaprofitability` | | NLP | Ipopt | | `malmquist` | |LP | GLPK | Where: - LP = Linear programming. - NLP = Nonlinear programming. - QP = Quadratic programming. Models can be solved using a different optimizer by passing a `DEAOptimizer` object to the `optimizer` optional argument. See [JuMP documentation](https://jump.dev/JuMP.jl/v0.21.6/installation/#Installing-a-solver) for a list of all available solvers. !!! warning "Choose a valid optimizer" The optimizer must support the problem type of the DEA model. For example, you cannot solve a Generalized Distance Function DEA model using the GLPK solver because it is a linear programming solver and `deagdf` requires a nonlinear programming solver. The following is an example of solving the radial DEA model using the `Ipopt` sovler: ```jldoctest julia> using Ipopt julia> using DataEnvelopmentAnalysis julia> X = [5 13; 16 12; 16 26; 17 15; 18 14; 23 6; 25 10; 27 22; 37 14; 42 25; 5 17]; julia> Y = [12; 14; 25; 26; 8; 9; 27; 30; 31; 26; 12]; julia> myoptimizer = DEAOptimizer(Ipopt.Optimizer, time_limit = 10, silent = true); julia> dea(X, Y, slack = false, optimizer = myoptimizer) Radial DEA Model DMUs = 11; Inputs = 2; Outputs = 1 Orientation = Input; Returns to Scale = CRS ────────────── efficiency ────────────── 1 1.0 2 0.62229 3 0.819856 4 1.0 5 0.310371 6 0.555555 7 1.0 8 0.757669 9 0.820106 10 0.490566 11 1.0 ────────────── ``` ### Optimizer API ```@docs DEAOptimizer newdeamodel ```