New Outer Approximation Algorithms for Nonsmooth Convex MINLP Problems

Abstract

This paper presents a novel outer approximation algorithm for nonsmooth mixed-integer nonlinear programming (MINLP) problems. The method proceeds by fixing the integer variables and solving the resulting nonlinear convex subproblem. When the subproblem is feasible, valid linear cuts are derived by computing suitable subgradients of the objective and constraint functions at the optimal solution, utilizing KKT optimality conditions. A new parameter, defined through the nonlinear constraint functions, is introduced to facilitate the generation of these cuts. For infeasible subproblems, a feasibility problem is solved, and valid linear cuts are generated via KKT-based subgradients to exclude the infeasible integer assignment. By integrating both types of cuts, a mixed-integer linear programming (MILP) master problem is formulated and proven equivalent to the original MINLP. This equivalence underpins a new outer approximation algorithm, which is guaranteed to terminate after a finite number of iterations. Numerical experiments on smooth convex MINLP problems demonstrate that the proposed algorithm produces tighter MILP relaxations than the classical outer approximation method. Furthermore, the approach offers an alternative mechanism for generating linear cuts, extending beyond reliance solely on first-order Taylor expansions and shows that the efficiency of outer approximation algorithm is strongly dependent on the inherent structure of the MINLP problem.