ACPbound

ACPbound is a framework and software library designed to provide rigorous, computable bounds for optimization problems under uncertainty. It combines deterministic and probabilistic bounding techniques to yield guarantees on performance, feasibility, or risk thresholds, aiming to support decision-making under imperfect information.

The system centers on a bound engine that propagates intervals and inequalities, a solver interface for external optimization engines, and a validation module for empirical checks. Core capabilities include deriving a priori bounds on objective values, computing a posteriori bounds after solutions are obtained, and automated bound refinement through adaptive subdivision or sampling. It supports linear, integer, and convex programs and can be coupled with existing solvers to extend their guarantees.

History and development of ACPbound trace to collaborative research efforts in the 2010s, involving contributors from multiple universities and research laboratories. The project name highlights its focus on adaptive and probabilistic bounds. The first public release occurred around 2020, with subsequent major updates expanding probabilistic bounding, interval arithmetic, and bound refinement methods. The project emphasizes transparency, reproducibility, and clear documentation of assumptions behind each bound.

Applications of ACPbound span operations research, engineering, machine learning safety, and financial risk assessment. It is used for robust planning under uncertainty, safety-margin analysis, and verifiable performance guarantees in deployed systems. The project is maintained as an open-source initiative with community contributions, ongoing documentation, and example workflows to assist practitioners in applying bound-based guarantees.

See also: robust optimization, interval arithmetic, probabilistic inequalities.