HiGHS optimization solver: Difference between revisions

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{{use British English|date=March 2022}}
{{use dmy dates|date=March 2022}}
 
{{infobox software
| title = HiGHS
| repo = {{url|https://github.com/ERGO-Code/HiGHS}}
| website = {{url|https://ergo-code.github.io/HiGHS}}
| programming language = [[C++]]
| license = [[MIT License|MIT]]
| genre = Optimization solver suite
| latest_release_version = 1.10.0
}}
 
{{infobox organization
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* Ivet Galabova
}}
| num_staff = 56
| budget =
| website = {{url|https://www.highs.dev}}
}}
 
{{infobox software
| title = HiGHS
| repo = {{url|https://github.com/ERGO-Code/HiGHS}}
| website = {{url|https://ergo-code.github.io/HiGHS}}
| programming language = [[C++]]
| license = [[MIT License|MIT]]
| genre = Optimization solver suite
| latest_release_version = 1.10.0
}}
 
'''HiGHS''' is open-source software to solve [[linear programming]] (LP), [[Integer programming|mixed-integer programming]] (MIP), and convex [[quadratic programming]] (QP) models.<ref name="hall-2020"/>
 
Written in [[C++]] and published under an [[MIT License|MIT]] license, HiGHS provides programming interfaces to [[C (programming language)|C]], [[Python (programming language)|Python]], [[Julia (programming language)|Julia]], [[Rust (programming language)|Rust]], [[R (programming language)|R]], [[JavaScript]], [[Fortran]], and [[C Sharp (programming language)|C#]]. It has no external dependencies. A{{nbsp}}convenient thin wrapper to Python is available via the {{url|https://pypi.org/project/highspy/|highspy}} [[Python Package Index|PyPI]] package. HiGHS is also callable via {{url|https://www[[NuGet]].<ref name="nuget.org/packages/Highs.Native/|nuget}}.-repo">
{{cite web
| title = Highs.Native
| url = https://nuget.org/packages/Highs.Native/
| access-date = 2025-05-13
}}
</ref>
 
Although generally single-threaded, some solver components can utilize multi-core architectures and, from {{URL|https://github.com/ERGO-Code/HiGHS/releases/tag/v1.10.0|Version 1.10.0}}, can run its first order LP solver on NVIDIA GPUs. HiGHS is designed to solve large-scale models and exploits [[sparse matrix|problem sparsity]]. Its performance relative to commercial and other open-source software is reviewed periodically using industry-standard [[Benchmark (computing)|benchmarks]].<ref name="mittelmann-benchmarks"/>
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=== Mixed integer programming ===
 
HiGHS has a [[branch and cut|branch-and-cut]] solver for MIP problems. Its performance relative to commercial and other open-source software is regularly reported using industry-standard benchmarks.<ref name="mittelmann-mip"/>
 
=== Quadratic programming ===