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

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)
• Interior point method: For continuous LP and QP
• PDLP: First-order primal-dual method (GPU-accelerated option available)
• Branch-and-cut: For MILP
• Active set: For QP

Quick Reference

Common Patterns

1. Basic Model Setup (Python with highspy)

import highspy
h = highspy.Highs()

# Read model from file
h.readModel("model.mps")

# Solve
h.run()

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

2. Building a Model Programmatically

import highspy
h = highspy.Highs()

# Add variables (one at a time)
h.addCol(cost=1.0, lower=0.0, upper=4.0, num_nz=0, index=None, value=None)  # x
h.addCol(cost=1.0, lower=1.0, upper=float('inf'), num_nz=0, index=None, value=None)  # y

# Set variable as integer
h.changeColIntegrality(1, highspy.HighsVarType.kInteger)

# Set objective sense
h.changeObjectiveSense(highspy.ObjSense.kMinimize)

# Solve
h.run()

3. Using with Julia JuMP

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...
optimize!(model)

4. Setting Options

h = highspy.Highs()

# Common options
h.setOptionValue("presolve", "on")
h.setOptionValue("time_limit", 100.0)
h.setOptionValue("mip_rel_gap", 0.01)
h.setOptionValue("solver", "ipm")  # interior point method

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

5. Hot Starting

# Set initial solution
h.setSolution(solution)

# Set initial basis
h.setBasis(basis)

# Run from warm start
h.run()

6. Extracting Model Information

# Get model dimensions
num_cols = h.getNumCols()
num_rows = h.getNumRows()
num_nz = h.getNumEntries()

# Get specific column/row data
col_data = h.getCol(col_index)
row_data = h.getRow(row_index)

# Get matrix coefficient
coeff = h.getCoeff(row_index, col_index)

7. Modifying Models

# Change objective coefficient
h.changeColCost(col_index, new_cost)

# Change bounds
h.changeColBounds(col_index, new_lower, new_upper)
h.changeRowBounds(row_index, new_lower, new_upper)

# Change matrix coefficient
h.changeCoeff(row_index, col_index, new_value)

Reference Files

This skill includes comprehensive documentation in references/:

• api.md - Api documentation
• getting_started.md - Getting Started documentation
• guide.md - Guide documentation
• options.md - Options documentation
• solvers.md - Solvers documentation
• terminology.md - Terminology documentation

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
• HighsSparseMatrix - Sparse matrix representation
• HighsHessian - Quadratic objective Hessian
• HighsSolution - Solution data
• HighsBasis - Basis status information
• HighsInfo - Solver statistics and info

Important Enums:

• HighsModelStatus - Model status (optimal, infeasible, unbounded, etc.)
• HighsVarType - Variable types (continuous, integer, semi-continuous, etc.)
• ObjSense - Objective sense (minimize, maximize)

Tolerances and Accuracy

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

• Primal feasibility tolerance - How close to feasible
• 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.

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

Callbacks

HiGHS supports callbacks for:

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

Working with This Skill

For Beginners

1. Start with references/getting_started.md for installation and basic concepts
2. Review problem formulations in references/terminology.md
3. Try basic examples from references/api.md

For Specific Features

• Building models: references/guide.md (Basic features section)
• API reference: references/api.md (C++, Python, Julia interfaces)
• Solver options: references/options.md
• Advanced topics: references/guide.md (GPU, hot start, presolve)
• Performance tuning: references/solvers.md

For Code Examples

• Python examples in references/api.md (Python section)
• Julia examples in references/api.md (Julia section)
• C++ examples in references/api.md (C++ section)

Performance Benchmarks

HiGHS is competitive with commercial solvers. See:

• Mittelmann LP benchmarks (feasibility and optimality)
• Mittelmann MILP benchmarks

Common Use Cases

1. Supply chain optimization - Minimize costs while meeting constraints
2. Resource allocation - Optimize resource distribution
3. Production planning - Maximize output or minimize waste
4. Portfolio optimization - Balance risk and return (QP)
5. Network flow problems - Transportation, routing
6. Scheduling - Job shop, employee scheduling (MILP)
7. Cutting stock problems - Minimize material waste

Resources

references/

Organized documentation extracted from official sources. These files contain:

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

scripts/

Add helper scripts here for common automation tasks.

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)

PDLP Reports Optimal But HiGHS Says Not Optimal

• PDLP uses relative tolerances vs absolute
• Increase kkt_tolerance to 1e-4
• 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)

Memory Issues with Large Models

• Use sparse matrix representations
• Pass models via passModel() rather than building incrementally
• Consider model compression techniques

Additional Resources

• GitHub: https://github.com/ERGO-Code/HiGHS
• Documentation: https://ergo-code.github.io/HiGHS/dev/
• Contact: [email protected]

Notes

• This skill was automatically generated from official HiGHS documentation
• Reference files preserve structure and examples from source docs
• Code examples include language detection for syntax highlighting
• All references link back to original documentation

Updating

To refresh this skill with updated documentation:

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