Primal MINLP Heuristics in a Nutshell
نویسنده
چکیده
Primal heuristics are an important component of state-of-the-art codes for mixed integer nonlinear programming (MINLP). In this article we give a compact overview of primal heuristics for MINLP that have been suggested in the literature of recent years. We sketch the fundamental concepts of different classes of heuristics and discuss specific implementations. A brief computational experiment shows that primal heuristics play a key role in achieving feasibility and finding good primal bounds within a global MINLP solver.
منابع مشابه
A Center-Cut Algorithm for Quickly Obtaining Feasible Solutions and Solving Convex MINLP Problems
Here we present a center-cut algorithm for convex mixed-integer nonlinear programming (MINLP) that can either be used as a primal heuristic or as a deterministic solution technique. Like many other algorithms for convex MINLP, the center-cut algorithm constructs a linear approximation of the original problem. The main idea of the algorithm is to use the linear approximation differently in order...
متن کاملUndercover: a primal MINLP heuristic exploring a largest sub-MIP
We present Undercover, a primal heuristic for nonconvex mixed-integer nonlinear programming (MINLP) that explores a mixed-integer linear subproblem (sub-MIP) of a given MINLP. We solve a vertex covering problem to identify a minimal set of variables that need to be fixed in order to linearize each constraint, a so-called cover. Subsequently, these variables are fixed to values obtained from a r...
متن کاملA Primal Heuristic for MINLP based on Dual Information
We present a novel heuristic algorithm to identify feasible solutions of a mixed-integer nonlinear programming problem arising in natural gas transportation: the selection of new pipelines to enhance the network’s capacity to a desired level in a cost-efficient way. We solve this problem in a linear programming based branch-and-cut approach, where we deal with the nonlinearities by linear outer...
متن کاملSCIP: Global Optimization of Mixed-Integer Nonlinear Programs in a Branch-and-Cut Framework
This paper describes the extensions that were added to the constraint integer programmingframework SCIP in order to enable it to solve convex and nonconvex mixed-integer nonlinearprograms (MINLPs) to global optimality. SCIP implements a spatial branch-and-bound algorithmbased on a linear outer-approximation, which is computed by convex overand underestimationof nonconvex functio...
متن کاملPredictive Control of Nonlinear Hybrid Systems Using Generalized Outer Approximation
This paper presents an efficient optimization algorithm for mixed integer nonlinear programming (MINLP) problem resulting from multiple partially linearized (MPL) model based control of nonlinear hybrid dynamical system (NHDS). The algorithm uses structural information of the canonical MPL framework and derives comparatively easier quadratic programming (QP) primal problem as well as an MILP ma...
متن کامل