parse-lp 3.2.0

A LP file format parser for Python, powered by Rust

parse_lp

A LP file format parser, writer, and modifier for Python, powered by Rust.

Features

• Complete LP Support: Handles all standard LP file format features
• Problem Modification: Programmatically modify objectives, constraints, and variables
• LP File Writing: Generate LP files from modified problems with round-trip compatibility
• Problem Analysis: Comprehensive statistics, structure analysis, and issue detection with configurable thresholds
• Easy Data Access: Direct access to problem components (variables, constraints, objectives)
• CSV Export: Export parsed data to CSV files for further analysis
• Problem Comparison: Compare two LP problems to identify differences
• Type Safety: Full type hints for better IDE support and development experience

Installation

pip install parse_lp

Quick Start

from parse_lp import LpParser

# Parse an LP file
parser = LpParser("path/to/problem.lp")
parser.parse()

# Access problem information
print(f"Problem: {parser.name}")
print(f"Sense: {parser.sense}")
print(f"Variables: {parser.variable_count()}")
print(f"Constraints: {parser.constraint_count()}")

# Modify the problem
parser.update_objective_coefficient("OBJ", "x1", 5.0)
parser.rename_variable("x2", "production")
parser.update_constraint_rhs("C1", 100.0)

# Write back to LP format
modified_lp = parser.to_lp_string()
parser.save_to_file("modified_problem.lp")

# Analyze problem structure and detect issues
analysis = parser.analyze()
print(f"Matrix density: {analysis['summary']['density']:.4f}")
print(f"Issues found: {len(analysis['issues'])}")

# Export to CSV files
parser.to_csv("output_directory/")

Usage Examples

Basic Parsing and Information

from parse_lp import LpParser

parser = LpParser("optimization_problem.lp")
parser.parse()

# Get problem overview
print(f"Problem Name: {parser.name}")
print(f"Optimization Sense: {parser.sense}")
print(f"Variables: {parser.variable_count()}")
print(f"Constraints: {parser.constraint_count()}")
print(f"Objectives: {parser.objective_count()}")

Accessing Problem Data

# Access objectives
for i, objective in enumerate(parser.objectives):
    print(f"Objective {i+1}: {objective['name']}")
    for coef in objective['coefficients']:
        print(f"  {coef['name']}: {coef['value']}")

# Access variables
for var_name, var_info in parser.variables.items():
    print(f"Variable {var_name}:")
    print(f"  Type: {var_info['var_type']}")
    if 'bounds' in var_info:
        bounds = var_info['bounds']
        if 'lower' in bounds:
            print(f"  Lower bound: {bounds['lower']}")
        if 'upper' in bounds:
            print(f"  Upper bound: {bounds['upper']}")

# Access constraints
for constraint in parser.constraints:
    print(f"Constraint {constraint['name']}:")
    print(f"  Type: {constraint['type']}")
    if constraint['type'] == 'standard':
        print(f"  Operator: {constraint['operator']}")
        print(f"  RHS: {constraint['rhs']}")
        print(f"  Coefficients: {len(constraint['coefficients'])}")

CSV Export

import os

# Create output directory
os.makedirs("output", exist_ok=True)

# Export to CSV files
parser.to_csv("output/")

# Files created:
# - output/variables.csv
# - output/constraints.csv
# - output/objectives.csv

Problem Analysis

Analyze problem structure, detect potential issues, and get comprehensive statistics.

from parse_lp import LpParser

parser = LpParser("problem.lp")
parser.parse()

# Get complete analysis
analysis = parser.analyze()

# Summary statistics
summary = analysis['summary']
print(f"Problem: {summary['name']}")
print(f"Sense: {summary['sense']}")
print(f"Variables: {summary['variable_count']}")
print(f"Constraints: {summary['constraint_count']}")
print(f"Nonzeros: {summary['total_nonzeros']}")
print(f"Matrix density: {summary['density']:.4f}")

# Sparsity metrics
sparsity = analysis['sparsity']
print(f"Variables per constraint: {sparsity['min_vars_per_constraint']} - {sparsity['max_vars_per_constraint']}")

# Variable type distribution
var_types = analysis['variables']['type_distribution']
print(f"Variable types: {var_types}")

# Coefficient ranges
coeffs = analysis['coefficients']
print(f"Constraint coefficients: {coeffs['constraint_coeff_range']}")
print(f"Objective coefficients: {coeffs['objective_coeff_range']}")
print(f"Coefficient ratio: {coeffs['coefficient_ratio']:.2f}")

# Check for issues (warnings and errors)
for issue in analysis['issues']:
    print(f"[{issue['severity']}] {issue['category']}: {issue['message']}")

Analysis with custom thresholds:

# Customize thresholds for issue detection
analysis = parser.analyze_with_config(
    large_coeff_threshold=1e8,      # Flag coefficients above this
    small_coeff_threshold=1e-10,    # Flag coefficients below this
    ratio_threshold=1e5             # Flag if max/min ratio exceeds this
)

Get issues only:

# Get just the detected issues without full analysis
issues = parser.get_issues()

for issue in issues:
    print(f"[{issue['severity']}] {issue['category']}: {issue['message']}")
    if issue['details']:
        print(f"  Details: {issue['details']}")

Issue types detected:

• Invalid variable bounds (lower > upper)
• Numerical scaling warnings (large coefficients, high ratios)
• Empty constraints (no variables)
• Unused variables (not in any constraint or objective)
• Fixed variables (lower bound = upper bound)
• Singleton constraints (only one variable)

Problem Modification

from parse_lp import LpParser

# Parse an existing LP file
parser = LpParser("optimization_problem.lp")
parser.parse()

# Modify objectives
parser.update_objective_coefficient("profit", "x1", 5.0)
parser.rename_objective("profit", "total_profit")

# Modify constraints
parser.update_constraint_coefficient("capacity", "x1", 2.0)
parser.update_constraint_rhs("capacity", 200.0)
parser.rename_constraint("capacity", "production_limit")

# Modify variables
parser.rename_variable("x1", "production_a")
parser.update_variable_type("production_a", "integer")

# Set problem properties
parser.set_problem_name("Modified Optimization Problem")
parser.set_sense("minimize")

# Write back to LP format
modified_lp_content = parser.to_lp_string()
parser.save_to_file("modified_problem.lp")

# Verify round-trip compatibility
new_parser = LpParser("modified_problem.lp")
new_parser.parse()
print(f"Successfully modified and re-parsed: {new_parser.name}")

Problem Comparison

# Parse two different versions of a problem
parser1 = LpParser("problem_v1.lp")
parser1.parse()

parser2 = LpParser("problem_v2.lp")
parser2.parse()

# Compare the problems
diff = parser1.compare(parser2)

print("Comparison Results:")
print(f"Name changed: {diff['name_changed']}")
print(f"Sense changed: {diff['sense_changed']}")
print(f"Variable count difference: {diff['variable_count_diff']}")
print(f"Added variables: {diff['added_variables']}")
print(f"Removed variables: {diff['removed_variables']}")
print(f"Modified variables: {diff['modified_variables']}")
print(f"Added constraints: {diff['added_constraints']}")
print(f"Removed constraints: {diff['removed_constraints']}")

Objectives Structure

[
    {
        "name": "objective_name",
        "coefficients": [
            {"name": "variable_name", "value": 1.5},
            {"name": "another_var", "value": -2.0}
        ]
    }
]

Variables Structure

{
    "variable_name": {
        "name": "variable_name",
        "var_type": "Continuous",  # or "Binary", "Integer", etc.
        "bounds": {  # Optional
            "lower": 0.0,  # Optional
            "upper": 100.0  # Optional
        }
    }
}

Constraints Structure

[
    {
        "name": "constraint_name",
        "type": "standard",  # or "sos"
        "sense": "LessOrEqual",  # "Equal", "GreaterOrEqual"
        "rhs": 10.0,
        "coefficients": [
            {"name": "variable_name", "value": 2.0}
        ]
    },
    {
        "name": "sos_constraint",
        "type": "sos",
        "sos_type": "S1",  # or "S2"
        "weights": [
            {"name": "var1", "value": 1.0},
            {"name": "var2", "value": 2.0}
        ]
    }
]

Modification Methods

Objective Methods

• update_objective_coefficient(obj_name, var_name, coefficient) - Update or add coefficient
• rename_objective(old_name, new_name) - Rename an objective
• remove_objective(obj_name) - Remove an objective

Constraint Methods

• update_constraint_coefficient(const_name, var_name, coefficient) - Update or add coefficient
• update_constraint_rhs(const_name, new_rhs) - Update right-hand side value
• rename_constraint(old_name, new_name) - Rename a constraint
• remove_constraint(const_name) - Remove a constraint

Variable Methods

• rename_variable(old_name, new_name) - Rename variable across problem
• update_variable_type(var_name, var_type) - Change variable type
• remove_variable(var_name) - Remove variable from problem

Problem Methods

• set_problem_name(name) - Set problem name
• set_sense(sense) - Set optimization sense ("maximize" or "minimize")

Writing Methods

• to_lp_string() - Generate LP format string
• to_lp_string_with_options(**options) - Generate with custom formatting
• save_to_file(filepath) - Save to LP file

Analysis Methods

• analyze() - Get complete problem analysis including statistics and issues
• analyze_with_config(large_coeff_threshold, small_coeff_threshold, ratio_threshold) - Analysis with custom thresholds
• get_issues() - Get only detected issues/warnings without full analysis

Variable Types

Supported variable types for update_variable_type():

• "binary" - Binary variables (0 or 1)
• "integer" - General integer variables
• "general" - Continuous variables (default)
• "free" - Free variables (no bounds)
• "semicontinuous" - Semi-continuous variables

Supported LP Format Features

• Multiple objective functions
• Standard constraints (≤, =, ≥)
• Variable bounds
• Variable types (continuous, binary, integer)
• SOS (Special Ordered Sets) constraints
• Problem names and comments
• Scientific notation in coefficients

License

Licensed under either of Apache License, Version 2.0 or MIT license at your option.

Contributing

Issues and pull requests are welcome at: https://github.com/dandxy89/lp_parser_rs

make build
make install
make unit-test