Tulip is an open-source interior-point solver for linear optimization, written in pure Julia. It implements the homogeneous primal-dual interior-point algorithm with multiple centrality corrections, and therefore handles unbounded and infeasible problems. Tulip’s main feature is that its algorithmic framework is disentangled from linear algebra implementations. This allows to seamlessly integrate specialized routines for structured problems.
Tulip is licensed under the MPL 2.0 license.
Install Tulip using the Julia package manager:
import Pkg
Pkg.add("Tulip")The recommended way of using Tulip is through JuMP or MathOptInterface (MOI).
The low-level interface is still under development and is likely change in the future. The MOI interface is more stable.
Tulip follows the syntax convention PackageName.Optimizer:
using JuMP
import Tulip
model = Model(Tulip.Optimizer)Linear objectives, linear constraints and lower/upper bounds on variables are supported.
The type Tulip.Optimizer is parametrized by the model's arithmetic, for example, Float64 or BigFloat.
This allows to solve problem in higher numerical precision.
See the documentation for more details.
import MathOptInterface as MOI
import Tulip
model = Tulip.Optimizer{Float64}() # Create a model in Float64 precision
model = Tulip.Optimizer() # Defaults to the above call
model = Tulip.Optimizer{BigFloat}() # Create a model in BigFloat precisionSee the documentation for a full list of parameters.
To set parameters in JuMP, use:
using JuMP, Tulip
model = Model(Tulip.Optimizer)
set_attribute(model, "IPM_IterationsLimit", 200)To set parameters in MathOptInterface, use:
using Tulip
import MathOptInterface as MOI
model = Tulip.Optimizer{Float64}()
MOI.set(model, MOI.RawOptimizerAttribute("IPM_IterationsLimit"), 200)To set parameters in the Tulip API, use:
using Tulip
model = Tulip.Model{Float64}()
Tulip.set_parameter(model, "IPM_IterationsLimit", 200)See app building instructions.
If you use Tulip in your work, we kindly ask that you cite the following reference (preprint available here).
@Article{Tulip.jl,
author = {Tanneau, Mathieu and Anjos, Miguel F. and Lodi, Andrea},
journal = {Mathematical Programming Computation},
title = {Design and implementation of a modular interior-point solver for linear optimization},
year = {2021},
issn = {1867-2957},
month = feb,
doi = {10.1007/s12532-020-00200-8},
language = {en},
url = {https://doi.org/10.1007/s12532-020-00200-8},
urldate = {2021-03-07},
}
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