clara-opt 0.2.2

Solver-intrinsic explanations and reoptimization for linear programming

CLARA — Classical LP Analysis for Reoptimization and Attribution

A solver-intrinsic explanation framework for linear programming. CLARA extracts explanations directly from the simplex basis inverse $B^{-1}$ — without machine learning surrogates, without inverse formulations — and unifies factual and counterfactual perspectives on LP explainability within a single geometric framework.

What CLARA does

CLARA contributes two central capabilities, complemented by two supporting modules:

• Factual–counterfactual duality — a formal bound showing that the basis robustness $d_0$, computable in $O(mn)$ time from $B^{-1}$ alone, lower-bounds the cost of any basis-changing counterfactual explanation. Connects classical sensitivity analysis with counterfactual explanations for optimization.
• Reoptimization pipeline — automatic change detection, Oguz-bound impact analysis, warm-start method selection (primal simplex, dual simplex, or parametric LP), and structured diff reports.
• Simultaneous sensitivity regions (supporting) — Chebyshev center of the basis-preserving polyhedron, the geometric foundation of the duality bound.
• Objective-change attribution (supporting) — first-order and Shapley decomposition that annotates diff reports during reoptimization.

CLARA also offers two solver backends:

• Internal Revised Simplex — full transparency, retains $B^{-1}$ at every pivot for downstream analysis (practical for $n \le 100$).
• HiGHS — production-grade reference solver; CLARA reconstructs $B^{-1}$ from the reported basis for larger problems.

Installation

pip install clara-opt

For development:

git clone https://github.com/yoonsik-jung-opt/clara-opt.git
cd clara-opt
pip install -e ".[dev]"

Quick Start

# Explain a production planning problem
clara explain examples/production.lp

# Brief summary
clara explain problem.lp --level brief

# JSON output
clara explain problem.lp --format json

# Use HiGHS engine for larger problems
clara explain problem.lp --engine highs

# Solve only (no explanation)
clara solve problem.lp

# Problem info
clara info problem.lp

Python API

from clara.io import read_lp
from clara.engine.simplex import RevisedSimplex
from clara.explain import Explainer

problem = read_lp("problem.lp")
state = RevisedSimplex(problem).solve()
report = Explainer().explain(state, problem=problem)
print(report.to_text())

Academic Use

CLARA contributes to the explainability of mathematical optimization solvers, a research area surveyed by De Bock et al. (2024) in the XAIOR framework.

A manuscript describing the framework is in preparation. Citation information will be added here when a preprint or peer-reviewed version is available.

License

MIT © 2026 Yoonsik