cvxpy-debug 0.1.0

Diagnostic tools for CVXPY optimization problems

cvxpy-debug

Diagnostic tools for CVXPY optimization problems. When your problem is infeasible, unbounded, or numerically inaccurate, cvxpy-debug tells you why and how to fix it.

Installation

pip install cvxpy-debug

Quick Start

import cvxpy as cp
import cvxpy_debug

# Create an infeasible problem
x = cp.Variable(3, nonneg=True)
constraints = [
    cp.sum(x) <= 100,
    x[0] >= 50,
    x[1] >= 40,
    x[2] >= 30,  # Sum of minimums = 120 > 100
]
prob = cp.Problem(cp.Minimize(cp.sum(x)), constraints)

# Debug it - automatically solves and diagnoses
report = cvxpy_debug.debug(prob)

Output:

════════════════════════════════════════════════════════════════
                     INFEASIBILITY REPORT
════════════════════════════════════════════════════════════════

Problem has 4 constraints. Found 4 that conflict.

CONFLICTING CONSTRAINTS
───────────────────────
  Constraint              Slack needed
  ────────────────────    ─────────────
  sum(x) <= 100           20.0
  x[0] >= 50              0.0
  x[1] >= 40              0.0
  x[2] >= 30              0.0

SUGGESTED FIXES
───────────────
• Increase budget to at least 120
• Reduce one of the minimum bounds

Features

• Infeasibility diagnosis: Find which constraints conflict using IIS (Irreducible Infeasible Subsystem)
• Unboundedness diagnosis: Identify which variables are unbounded and in which direction
• Numerical issues: Detect scaling problems, ill-conditioning, and constraint violations
• Performance analysis: Detect anti-patterns like loop-generated constraints
• Full cone support: Linear, SOC, SDP, and exponential cone constraints
• Human-readable reports: Clear explanations and actionable fix suggestions

Examples

See the examples/ folder for comprehensive usage examples:

Quick Start Examples

• basic_infeasibility.py - Diagnose conflicting constraints
• basic_unboundedness.py - Diagnose unbounded objectives
• basic_numerical.py - Diagnose scaling issues
• basic_performance.py - Detect performance anti-patterns

Problem Type Examples

• infeasibility_diagnosis.py - Full IIS workflow
• unboundedness_diagnosis.py - Ray analysis and bounds
• numerical_issues.py - Conditioning and violations
• cone_constraints.py - SOC, PSD, ExpCone examples

Real-World Scenarios

• portfolio_optimization.py - Markowitz mean-variance
• resource_allocation.py - Budget allocation
• scheduling.py - Task scheduling
• regression.py - Constrained regression

API

Main Function

cvxpy_debug.debug(
    problem,
    solver=None,              # Override solver for diagnostic solves
    find_minimal_iis=False,   # Find minimal conflicting constraint set
    include_conditioning=False,  # Analyze condition numbers (slower)
    include_performance=True,    # Include performance analysis
)

Focused Diagnostics

# Infeasibility analysis
cvxpy_debug.debug_infeasibility(problem, report)

# Unboundedness analysis
cvxpy_debug.debug_unboundedness(problem, report)

# Numerical issues analysis
cvxpy_debug.debug_numerical_issues(problem, report)

# Performance analysis
cvxpy_debug.debug_performance(problem, report)

License

Apache 2.0