HiGHS

The open-source LP/MIP/QP solver from the University of Edinburgh, MIT-licensed, C++11. Until this page the spoke had a metaheuristics corpus with no solver in it — linear-programming and integer-programming rested on Wikipedia, and nothing here could actually be run. T1 (the project’s own documentation).

What it is

Four separate solvers under one library, each with a named author:

Julian Hall runs the project; Ivet Galabova maintains the engineering. Built for large-scale sparse models, serial and parallel. Interfaces from C, C#, Fortran, Julia and Python, plus a standalone executable on Windows, Linux and macOS. That interface list is why HiGHS is the default solver behind SciPy’s linprog and much of the Python optimization stack — most people running it have never typed its name.

The site claims “high performance” and publishes no numbers of its own, which is the ordinary vendor-documentation problem. Mittelmann supplies them.

Where it actually places (Mittelmann, 2026)

LPopt (optimal basic solution, 65 disclosed instances, 2026-07-01) — scaled shifted geometric mean, COPT as 1.0:

MILP (240 MIPLIB2017 instances, 2 h limit, Ryzen 9 5900X 12-core, 2026-07-07):

Two readings. First, HiGHS is the strongest fully open solver in both tables — it beats CLP by 2×, GLOP by 4.7× and SoPlex by 8× on LP, and beats SCIP on MILP while solving 22 more instances. Second, the gap to the commercial front is not small: 12.8× on LP, 7.55× on MILP, and the free solver leaves 82 of 240 MILP instances unsolved inside two hours that COPT finishes. Open-source has won the usable default position, not the fastest one.

Read the ranking with mittelmann-benchmarks’ own caveat attached: Gurobi, CPLEX and Xpress do not appear in these tables because their vendors demanded removal. The commercial baseline here is COPT — the fastest solver that still permits publication, which is not necessarily the fastest solver.

Why it matters to this spoke

The corpus is almost entirely metaheuristics — GA, PSO, simulated-annealing and the long tail of nature-inspired variants, all of which return good answers with no optimality guarantee. HiGHS is the other tradition: for problems that fit the LP/MIP form, an exact method with a proof, benchmarked on a public library (miplib) at a scale the metaheuristics literature never attempts. The no-free-lunch-theorem applies across problem classes; within the class where simplex and branch-and-cut apply, structure beats search and it isn’t close.

Related

linear-programming · integer-programming · mittelmann-benchmarks · miplib · convex-optimization · metaheuristic-optimization · no-free-lunch-theorem · synthesis