Solving MPS Files

HPRLP can directly read and solve linear programming problems in MPS (Mathematical Programming System) format, a widely-used industry standard format.

Quick Start

using HPRLP

# Step 1: Build model from MPS file
model = build_from_mps("path/to/problem.mps")

# Step 2: Configure solver parameters
params = HPRLP_parameters()
params.stoptol = 1e-4

# Step 3: Optimize
result = optimize(model, params)

Working with MPS Files

Basic Usage

using HPRLP

# Build the model
model = build_from_mps("model.mps")

# Set up parameters
params = HPRLP_parameters()

# Solve
result = optimize(model, params)

if result.status == "OPTIMAL"
    println("Found optimal solution!")
    println("Objective: ", result.primal_obj)
    println("Solution vector: ", result.x)
else
    println("Solver stopped with status: ", result.status)
end

With Custom Parameters

# Build model
model = build_from_mps("large_problem.mps")

# Configure parameters
params = HPRLP_parameters()
params.stoptol = 1e-9          # Higher accuracy
params.time_limit = 3600       # 1 hour time limit
params.use_gpu = true          # Enable GPU
params.verbose = true          # Show progress
params.warm_up = true          # Enable warmup for accurate timing

# Solve
result = optimize(model, params)

Common MPS Sources

NETLIB

The classic NETLIB LP test set:

• Download from: http://www.netlib.org/lp/data/
• Contains problems of various sizes and characteristics
• Standard benchmark for LP solvers

MIPLIB

Mixed-Integer Programming Library (LP relaxations):

• Download from: https://miplib.zib.de/
• Includes continuous LP problems
• Challenging real-world instances

Hans Mittelmann's Benchmark

Collection of LP and optimization problems:

• http://plato.asu.edu/ftp/lptestset/
• Regularly updated
• Various problem classes

Performance Tips

1. GPU vs CPU:

   • Use GPU for large problems (> 10,000 variables/constraints)
   • Use CPU for small to medium problems or when GPU is unavailable

2. Tolerance:

   • Use 1e-6 or 1e-8 for high-accuracy requirements

3. Time Limits:

   • Set reasonable time limits for batch processing
   • Default is 3600 seconds (1 hour)

4. Scaling:

   • Keep scaling enabled for better numerical stability
   • Disable only if you have pre-scaled data

See Also

• Parameters - Complete guide to solver parameters and configuration
• Output & Results - Understanding and interpreting solver results