Tutorials and how-to guides

This tutorial showcases some advanced features of solvers in JSOSolvers..

In this tutorial we illustrate the main uses of SolverBenchmark.

One of the main strengths of Julia for scientific computing is its native usage of arbitrary precision arithmetic, the same can be exploited for optimization models and solvers.

This package provides optimization solvers curated by the JuliaSmoothOptimizers organization.

A Julia repository of bundle adjustment problems from the Bundle Adjustment in the Large repository.

In this tutorial you will learn how to use JSO-compliant solvers to solve a PDE-constrained optimization problem discretized with PDENLPModels.jl.

Here is a short tutorial on using callbacks with our solvers.

LDLFactorizations.jl is a translation of Tim Davis's Concise LDLᵀ Factorization, part of SuiteSparse with several improvements.

NLPModelsTest has a new feature to check allocations of NLPModels.

CaNNOLeS is a solver for equality-constrained nonlinear least-squares problems.

NLPModelsIpopt is a thin IPOPT wrapper for NLPModels.

NLPModelsJuMP is a combination of NLPModels and JuMP.

OptimizationProblems is a collection of problems in JuMP and ADNLPModel format.

ADNLPModel is simple to use and is useful for classrooms.

A package to optimize linear and quadratic problems in QuadraticModel format using a regularized interior-point method.

This is a simple package to plot mathematical performance and data profiles, as defined by Elizabeth Dolan, Jorge Moré and Stefan Wild

CUTEst.jl was the first package in the JSO ecosystem. It is an essential part of the nonlinear optimization academic research landscape.

Learn about QuadraticModels.jl

Solve a discretized PDE-constrained optimization problem using DCISolver and Ipopt.

Learn about LinearOperators.jl

A thorough guide for developers, from 0 to benchmarking.

Quick guide to help you write your problem and solve it using Ipopt

Write a simple model with total control over its derivatives.