name	highs
description	High Performance Optimization Software for solving linear programming (LP), mixed integer linear programming (MILP), and quadratic programming (QP) problems

HiGHS - High Performance Optimization Software

Comprehensive assistance with HiGHS development, generated from official documentation.

HiGHS is open-source software for defining, modifying, and solving large-scale sparse linear optimization models. It's freely available under MIT license with no third-party dependencies.

When to Use This Skill

Use this skill when:

Solving linear programming (LP) problems
Solving mixed integer linear programming (MILP) problems
Solving quadratic programming (QP) problems
Implementing optimization solutions in C++, Python (highspy), Julia, C, C#, Fortran, or Rust
Building or modifying optimization models programmatically
Working with MPS or CPLEX LP file formats
Optimizing performance with parallel solvers or GPU acceleration
Setting solver options and tolerances
Hot-starting from existing solutions or bases
Extracting model information and solution data
Modifying existing optimization models

Problem Types Supported

HiGHS can solve problems of the form:

Linear Programming (LP):

minimize    c^T x
subject to  L ≤ Ax ≤ U
            l ≤ x ≤ u

Mixed Integer Linear Programming (MILP): Same as LP, but some variables must take integer values.

Quadratic Programming (QP): LP with additional objective term ½x^T Q x where Q is positive semi-definite. (Note: Cannot solve integer QP problems)

Solvers Available

Simplex methods: Revised simplex (primal and dual) - Most robust for general LP
Interior point method: Two implementations (IPX serial, HiPO parallel)
PDLP: First-order primal-dual method (GPU-accelerated option available)
Branch-and-cut: For MILP
Active set: For QP

Quick Reference

1. Basic Setup and Solve (Python)

Initialize HiGHS and solve a model from file:

import highspy
import numpy as np
h = highspy.Highs()

# Read a model from MPS file
filename = 'model.mps'
status = h.readModel(filename)
print('Reading model file', filename, 'returns a status of', status)

# Solve the model
h.run()

# Get solution
solution = h.getSolution()
info = h.getInfo()

2. Building a Simple Model (Python Simplified Interface)

Build an optimization model programmatically:

# Problem:
#   minimize    f  =  x0 +  x1
#   subject to              x1 <= 7
#               5 <=  x0 + 2x1 <= 15
#               6 <= 3x0 + 2x1
#               0 <= x0 <= 4; 1 <= x1

import highspy
h = highspy.Highs()

x0 = h.addVariable(lb = 0, ub = 4)
x1 = h.addVariable(lb = 1, ub = 7)

h.addConstr(5 <=   x0 + 2*x1 <= 15)
h.addConstr(6 <= 3*x0 + 2*x1)

h.minimize(x0 + x1)

3. Building a MILP Model (Julia C API)

Complete MILP example using Julia's C API wrapper:

using HiGHS

highs = Highs_create()
ret = Highs_setBoolOptionValue(highs, "log_to_console", false)
@assert ret == 0  # If ret != 0, something went wrong

# Add columns (variables)
Highs_addCol(highs, 1.0, 0.0, 4.0, 0, C_NULL, C_NULL)   # x is column 0
Highs_addCol(highs, 1.0, 1.0, Inf, 0, C_NULL, C_NULL)   # y is column 1

# Set y as integer variable
Highs_changeColIntegrality(highs, 1, kHighsVarTypeInteger)

# Set objective to minimize
Highs_changeObjectiveSense(highs, kHighsObjSenseMinimize)

# Solve
Highs_run(highs)

4. Using with Julia JuMP (High-Level Interface)

using JuMP
import HiGHS

model = Model(HiGHS.Optimizer)
set_optimizer_attribute(model, "presolve", "on")
set_optimizer_attribute(model, "time_limit", 60.0)

# Define your optimization model...
@variable(model, x >= 0)
@variable(model, y >= 0)
@objective(model, Min, x + y)
@constraint(model, 5 <= x + 2*y <= 15)

optimize!(model)

5. Extracting Solution Values Efficiently (Python)

Important: Direct array access is slow in Python. Convert to list first!

import highspy
h = highspy.Highs()
h.readModel('model.mps')
h.run()

# Get solution object
solution = h.getSolution()

# SLOW: Accessing directly from solution.col_value
# for i in range(num_cols):
#     val = solution.col_value[i]  # Takes 0.04s

# FAST: Convert to list first
col_value = list(solution.col_value)
for i in range(num_cols):
    val = col_value[i]  # Takes 0.0001s (400x faster!)

6. Setting Options and Choosing Solver

import highspy
h = highspy.Highs()

# Set common options
h.setOptionValue("presolve", "on")
h.setOptionValue("time_limit", 100.0)
h.setOptionValue("mip_rel_gap", 0.01)

# Choose specific solver
h.setOptionValue("solver", "simplex")  # or "ipm", "pdlp", "hipo"

# For GPU acceleration with PDLP
h.setOptionValue("solver", "pdlp")
h.setOptionValue("kkt_tolerance", 1e-4)  # Recommended for PDLP

7. Command Line Usage (Executable)

# Basic solve
$ bin/highs model.mps

# With options file
$ bin/highs --options_file=my_options.txt model.mps

# Write solution to file
$ bin/highs --solution_file=solution.txt model.mps

# See all command line options
$ bin/highs --help

Example options file (my_options.txt):

solver = pdlp
kkt_tolerance = 1e-4
presolve = on
time_limit = 300

8. C API - Adding Variables and Constraints

// Add a single column (variable)
Highs_addCol(highs, cost, lower, upper, num_new_nz, index, value)

// Add multiple columns
Highs_addCols(highs, num_new_col, costs, lower, upper, num_new_nz,
              starts, index, value)

// Change coefficient in constraint matrix
Highs_changeCoeff(highs, row, col, new_value)

// Change variable bounds
Highs_changeColBounds(highs, col, new_lower, new_upper)

// Change objective coefficient
Highs_changeColCost(highs, col, new_cost)

// Set variable as integer
Highs_changeColIntegrality(highs, col, kHighsVarTypeInteger)

9. Building from Source

# Clone repository
git clone https://github.com/ERGO-Code/HiGHS.git

# Build with CMake
cd HiGHS
cmake -S. -B build
cmake --build build --parallel

# For C# support
cmake -S. -Bbuild -DCSHARP=ON

10. Installation via Package Managers

# Python
$ pip install highspy

# Julia
julia> using Pkg
julia> Pkg.add("HiGHS")

# C# (NuGet)
$ dotnet add package Highs.Native --version 1.12.0

# Linux (install dependencies for HiPO)
$ sudo apt update
$ sudo apt install libopenblas-dev

Reference Files

This skill includes comprehensive documentation in references/:

api.md (15 pages)

Complete API reference for all language interfaces:

C++ interface: Building from source, class-based API
Python (highspy): Installation, examples, efficient value extraction
Julia: JuMP integration, C API wrapper
C API: Complete function reference for all operations
C#: NuGet package, build instructions
Data structures: HighsLp, HighsModel, HighsSparseMatrix, HighsHessian
Enums: Model status, variable types, solver options

getting_started.md (3 pages)

Overview of HiGHS capabilities (LP, MILP, QP)
Installation methods (source, package managers, binaries)
File format support (MPS, LP, gzip)
Executable usage and command-line options
Citation information

guide.md (4 pages)

Advanced features and usage patterns:

Basic features: Defining models, solving, extracting results
Further features: Model modification, hot starting, presolve
GPU acceleration: PDLP solver setup with CUDA
Feasibility and optimality: Understanding tolerances (absolute vs relative)

options.md (2 pages)

Complete list of HiGHS options
How to set options (file, command line, API)
Important options: presolve, solver, parallel, time_limit, tolerances

solvers.md (1 page)

Detailed solver descriptions (simplex, IPM, PDLP)
When to use each solver
Performance characteristics
Academic references

terminology.md (1 page)

Optimization terminology explained
Bounds, constraints, feasible region
Sparse matrices
Primal and dual values
Basic solutions and sensitivity

Use view to read specific reference files when detailed information is needed.

Key Concepts

Installation

Via Package Managers:

Python: pip install highspy or conda install highs
Julia: using Pkg; Pkg.add("HiGHS")
C#: NuGet package available
Rust: Available via cargo

From Source:

git clone https://github.com/ERGO-Code/HiGHS.git
cd HiGHS
cmake -S. -B build
cmake --build build --parallel

File Formats Supported

.mps - MPS format (industry standard)
.lp - CPLEX LP format
.gz - Compressed files (gzip)

Data Structures

Key Classes:

HighsLp - Linear programming model data
HighsModel - General optimization model (includes QP)
HighsSparseMatrix - Sparse matrix representation
HighsHessian - Quadratic objective Hessian
HighsSolution - Solution data (primal/dual values)
HighsBasis - Basis status information
HighsInfo - Solver statistics and convergence info

Important Enums:

HighsModelStatus - Model status (optimal, infeasible, unbounded, etc.)
HighsVarType - Variable types (continuous, integer, semi-continuous, etc.)
ObjSense - Objective sense (minimize, maximize)
HighsStatus - Return status from API calls

Tolerances and Accuracy

HiGHS uses absolute tolerances by default (default: 1e-7):

Primal feasibility tolerance - How close constraints must be satisfied
Dual feasibility tolerance - Dual constraint violations
Optimality tolerance - KKT condition satisfaction

Important: PDLP uses relative tolerances. For PDLP, increase kkt_tolerance to 1e-4 for faster convergence with acceptable accuracy.

GPU Acceleration

PDLP solver can run on NVIDIA GPUs (Linux/Windows only):

Requires CUDA Toolkit and matching NVIDIA driver
Must build HiGHS locally with CMake
Set solver to "pdlp" and adjust tolerances
Verify CUDA: nvcc --version

Health Warning: PDLP may not achieve same accuracy as simplex/IPM. Check HighsInfo for actual feasibility values.

Callbacks

HiGHS supports callbacks for:

Logging custom output
Implementing custom termination criteria
Monitoring solver progress
Extracting intermediate solutions

Working with This Skill

For Beginners

Start with references/getting_started.md for installation and basic concepts
Review problem formulations in references/terminology.md
Try Quick Reference Example 2 (simple Python model)
Learn file-based solving with Quick Reference Example 1

For Intermediate Users

Building models: See examples in references/api.md (Python section)
Solver options: Read references/options.md for tuning
Model modification: Check references/guide.md (Further features)
Hot starting: Use previous solutions to speed up solving

For Advanced Users

API reference: references/api.md for language-specific details
Performance tuning: references/solvers.md - choose optimal solver
GPU acceleration: references/guide.md (GPU section)
Tolerances: references/guide.md (Feasibility and optimality)

For Code Examples

Python: references/api.md (Python section) - Complete highspy examples
Julia: references/api.md (Julia section) - JuMP and C API
C++: references/api.md (C++ section) - Native library
C API: references/api.md (C section) - Low-level interface

Performance Benchmarks

HiGHS is competitive with commercial solvers. See:

Mittelmann LP benchmarks (feasibility and optimality)
Mittelmann MILP benchmarks

Common Use Cases

Supply chain optimization - Minimize costs while meeting constraints
Resource allocation - Optimize resource distribution
Production planning - Maximize output or minimize waste
Portfolio optimization - Balance risk and return (QP)
Network flow problems - Transportation, routing
Scheduling - Job shop, employee scheduling (MILP)
Cutting stock problems - Minimize material waste
Energy systems - Power generation and distribution

Resources

references/

Organized documentation extracted from official sources. These files contain:

Detailed explanations of all features
Code examples with language annotations
Links to original documentation
Table of contents for quick navigation

scripts/

Add helper scripts here for common automation tasks (e.g., batch solving, result analysis).

assets/

Add templates, boilerplate, or example projects here.

Citing HiGHS

If you use HiGHS in an academic context, please cite:

Parallelizing the dual revised simplex method Q. Huangfu and J. A. J. Hall, Mathematical Programming Computation, 10 (1), 119-142, 2018. DOI: 10.1007/s12532-017-0130-5

Troubleshooting

Model Status Not Optimal

Check HighsInfo for infeasibility/unboundedness indicators
Review constraint feasibility and objective bounds
Try different solvers (simplex, IPM, PDLP)
Verify model data is correct (no NaN, Inf values)

PDLP Reports Optimal But HiGHS Says Not Optimal

PDLP uses relative tolerances vs absolute
Increase kkt_tolerance to 1e-4 (recommended)
Check HighsInfo for actual infeasibility values
Consider if the solution is "good enough" for your use case

Slow Performance

Enable presolve: setOptionValue("presolve", "on")
Try parallel mode for large problems
Adjust time limits and MIP gap tolerances
Consider GPU acceleration for very large LPs (PDLP)
Try different solver: simplex vs IPM vs PDLP

Memory Issues with Large Models

Use sparse matrix representations
Pass models via passModel() rather than building incrementally
Consider model compression techniques
Use compressed input files (.mps.gz)

Python Performance Issues

Critical: Convert solution arrays to lists before iteration (see Example 5)
Use numpy arrays when appropriate
Avoid repeated API calls in loops

Can't Read LP/MPS File

Check file format (lpsolve format NOT supported)
Try compressed version (.gz works, .zip does not)
Verify file path is correct

Additional Resources

GitHub: https://github.com/ERGO-Code/HiGHS
Documentation: https://ergo-code.github.io/HiGHS/dev/
Contact: highsopt@gmail.com
Issues: File bugs at GitHub Issues

Notes

This skill was automatically generated from official HiGHS documentation (v1.10.0+)
Reference files preserve structure and examples from source docs
Code examples include language detection for syntax highlighting
All references link back to original documentation
Examples tested with HiGHS v1.10.0 and later

Updating

To refresh this skill with updated documentation:

Re-run the scraper: python3 cli/doc_scraper.py --config configs/highs.json
The skill will be rebuilt with the latest information

Install Skill

SKILL.md