A nonlinear programming solver based on the filter line-search interior point method (as in Ipopt) that can handle/exploit diverse classes of data structures, either on host or device memories.
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The following example shows how to solve the HS15 problem with JuMP and MadNLP:
using JuMP, MadNLP
model = Model()
@variable(model, x1 <= 0.5)
@variable(model, x2)
@objective(model, Min, 100.0 * (x2 - x1^2)^2 + (1.0 - x1)^2)
@constraint(model, x1 * x2 >= 1.0)
@constraint(model, x1 + x2^2 >= 0.0)
JuMP.set_optimizer(model, MadNLP.Optimizer)
JuMP.set_optimizer_attribute(model, "max_iter", 100)
JuMP.set_optimizer_attribute(model, "print_level", MadNLP.INFO)
optimize!(model)MadNLP can be installed directly via the Julia package manager:
pkg> add MadNLPFurthermore, MadNLP comes with several extensions:
MadNLPGPU: import GPU-accelerated linear solvers in MadNLPMadNLPHSL: import the HSL linear solvers in MadNLPMadNLPPardiso: import the Pardiso linear solver in MadNLP
If you use MadNLP.jl in your research, we would greatly appreciate your citing it.
@article{shin2024accelerating,
title = {Accelerating optimal power flow with {GPU}s: {SIMD} abstraction of nonlinear programs and condensed-space interior-point methods},
author = {Shin, Sungho and Anitescu, Mihai and Pacaud, Fran{\c{c}}ois},
journal = {Electric Power Systems Research},
volume = {236},
pages = {110651},
year = {2024},
publisher = {Elsevier}
}
@article{shin2021graph,
title = {Graph-based modeling and decomposition of energy infrastructures},
author = {Shin, Sungho and Coffrin, Carleton and Sundar, Kaarthik and Zavala, Victor M},
journal = {IFAC-PapersOnLine},
volume = {54},
number = {3},
pages = {693--698},
year = {2021},
publisher = {Elsevier}
}- Please report issues and feature requests via the GitHub issue tracker.
- Questions are welcome at GitHub discussion forum.