Tightening the mixed integer linear formulation for the piecewise linear approximation in general dimensions

TL;DR

This paper proposes new strategies to tighten MILP formulations for piecewise linear approximations in multiple dimensions, significantly improving solution efficiency by leveraging the structure of well-behaved CPWL interpolations.

Contribution

It introduces the concept of well-behaved CPWL interpolations and presents six strategies to tighten MILP formulations, enhancing computational performance.

Findings

Significant reduction in solution times with combined tightening strategies

Effective use of the DC representation for problem tightening

Demonstrated improvements across various data sets

Abstract

This paper addresses the problem of tightening the mixed-integer linear programming (MILP) formulation for continuous piecewise linear (CPWL) approximations of data sets in arbitrary dimensions. The MILP formulation leverages the difference-of-convex (DC) representation of CPWL functions. We introduce the concept of well-behaved CPWL interpolations and demonstrate that any CPWL interpolation of a data set has a well-behaved version. This result is critical to tighten the MILP problem. We present six different strategies to tighten the problem, which include fixing the values of some variables, introducing additional constraints, identifying small big-M parameter values and applying tighter variable bounds. These methods leverage key aspects of the DC representation and the inherent structure of well-behaved CPWL interpolations. Experimental results demonstrate that specific combinations of these tightening strategies lead to significant improvement in solution times, especially for tightening strategies that consider well-behaved CPWL solutions.