Domain Reduction Techniques for Global NLP and MINLP Optimization 2

Domain reduction techniques for global NLP and MINLP optimization Y. Puranik1 and Nikolaos V. Sahinidis1 1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA Abstract Optimization solvers routinely utilize presolve techniques, including model simplification, reformulation and domain reduction techniques. Domain reduction techniques are especially important in speeding up convergence to the global optimum for challenging nonconvex nonlinear programming (NLP) and mixed- integer nonlinear programming (MINLP) optimization problems. In this work, we survey the various techniques used for domain reduction of NLP and MINLP optimization problems. We also present a computational analysis of the impact of these techniques on the performance of various widely available global solvers on a collection of 1740 test problems. Keywords: Constraint propagation; feasibility-based bounds tightening; optimality-based bounds tightening; domain reduction 1 Introduction combinatorial complexity introduced by the integer variables, nonconvexities in objective function or We consider the following mixed-integer nonlinear the feasible region lead to multiple local minima programming problem (MINLP): and provide a challenge to the optimization of such problems. min f(~x) Branch-and-bound [138] based methods can be s.t. g(~~x) ≤ 0 exploited to solve Problem 1 to global optimal- (1) ~xl ≤ ~x ≤ ~xu ity. Inspired by branch-and-bound for discrete Rn−m Zm ~x ∈ × programming problems [109], branch-and-bound was arXiv:1706.08601v1 [cs.DS] 26 Jun 2017 adapted for continuous problems by Falk and MINLP is a very general representation for opti- Soland [58]. The algorithm proceeds by bounding mization problems and includes linear programming the global optimum by a valid lower and upper (LP), mixed-integer linear programming (MIP) and bound throughout the search process. Whenever nonlinear programming (NLP) in its subclasses. A these bounds are within an acceptable tolerance, the variety of applications in diverse fields are routinely algorithm terminates with the upper bound as a formulated using this framework including water (near-)global optimum. The algorithm exhaustively network design [94, 62], hydro energy systems searches the space by branching on variables to management [44], protein folding [143], robust divide the space into subdomains. The lower and control [18], trim loss [84], heat exchanger network upper bounding procedures are recursively applied synthesis [69], gas networks [128], transportation [68], to the new subdomains in a tree search until the chemical process operations [76], chemical process bounds converge. Branch-and-bound algorithms are synthesis [77], crystallographic imaging [178], and known to terminate within ǫ-accuracy (ǫ > 0) to a seizure predictions [148]. Modelling via nonconvex global optimum as long as branching, node selection objective functions or constraints is necessitated for and lower bounding are designed to satisfy certain many of these practical applications. Aside from the conditions [89]. For special cases, the true global ©2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. The formal publication of this article is available at https://link.springer.com/article/10.1007/s10601-016-9267-5. Domain reduction techniques for global NLP and MINLP optimization 2 optimum can be finitely achieved with branch-and- bound [173, 8, 38]. The success of branch-and-bound methods for global optimization is evident from the numerous software implementations available, including ANTIGONE [135], BARON [185], Couenne [25], LindoGlobal [117] and SCIP [3]. In this work, we survey the various domain reduction techniques that are employed within branch-and-bound algorithms. While these techniques are not necessary to ensure convergence to the global optimum, they typically speed up convergence. These techniques often exploit Reduced domain feasibility analysis to eliminate infeasible parts of the search space. Alternatively, the methods can also utilize optimality arguments to shrink the Original domain search space while ensuring at least one optimal solution is retained. Domain reduction techniques Figure 1: Reduction in domains leads to tighter constitute the major component of the solution methods for satisfiability problems through unit relaxations propagation [126] and for constraint programming (CP) through various filtering algorithms that achieve differing levels of consistencies [29]. They communities wherever applicable. We also present are also exploited in artificial intelligence (AI) [54] results on standard test libraries with different and interval analysis [142, 98]. Some of the other global solvers to demonstrate the impact of domain names used in the literature for these methods include reduction strategies on their performance. bound propagation, bounds tightening, bound The remainder of the paper is organized as strengthening, domain filtering, bound reduction and follows. Representation of general MINLPs through range reduction. factorable reformulations and directed acyclic graphs Mathematical-programming-based methods for is described in Section 2. Methods for constraint solving nonconvex MINLPs often rely on the solution propagation and bounds tightening in global opti- of a relaxation problem for finding a valid lower mization of MINLPs often rely on interval arithmetic. bound. The strength of the relaxations employed A brief introduction to this topic is provided in for lower bounding depends on the diameter of the Section 3. We introduce presolving for optimization feasible region. Smaller domains lead to tighter in Section 4. Domain reduction techniques that relaxations. For example, Figure 1 shows the convex rely on eliminating infeasible regions for the problem relaxation for a simple univariate concave function. are described in Section 5. Techniques that A convex relaxation for a given function defined on utilize optimality arguments for carrying out domain a nonempty convex set is a convex function that reduction are described in Section 6. Computational underestimates the given function on its domain. The tradeoffs in the implementation of some of the convex relaxation over the reduced domain provides advanced techniques are discussed in Section 7. The a better approximation for the univariate concave computational impact of many of these techniques function thereby providing better lower bounds. on the performance of widely available solvers is Domain reduction techniques not only reduce the investigated in Section 8. Finally, we conclude in diameter of the search space, but they also improve Section 9. the tightness of convex relaxations. Domain reduction techniques have been exten- 2 Representation sively studied in various communities including AI, CP, mathematical programming, interval analysis A crucial step in the branch-and-bound algorithm is and computer science where they can be viewed the construction of relaxations. One of the most as chaotic iterations [14]. The primary objective widely used methods for this purpose is the idea of this paper is to review key domain reduction of factorable reformulations. It involves splitting techniques applicable to (nonconvex) NLPs and a problem into basic atomic functions that are MINLPs and point to connections between methods utilized for computing the function, an idea exploited from constraint programming and interval arithmetic Puranik and Sahinidis 3 by McCormick [131], who developed a technique 167]. In this graph representation, variables (x,y,z) that constructed non-differentiable relaxations of and constants are leaf nodes, vertices are elementary optimization problems. Ryoo and Sahinidis [159] operations (+, −, ∗,/, log, exp, etc.) and the gain differentiability by introducing new variables functions to be represented are the root nodes. and equations for each of the intermediate functional Variable and constraint bounds are represented forms. These functional forms are simple in nature through suitable intervals for the root and leaf nodes. like the bilinear term. A similar idea was proposed Common subexpressions are combined to reduce the by Kearfott [97] where new variables and equations size of the graph as doing so is known to tighten are introduced to decompose nonlinearities to allow the resulting relaxations [185, Theorem 2]. Different for more accurate computations of interval Jacobian mathematical formulations can be generated from the matrices. same DAG depending on the needs of the solver. Consider the following example: Expressions are evaluated by propagating values from the leaves to the root node through the edges in a min 3x +4y forward mode. Backward propagation is utilized for x − y ≤ 4 the computation of slopes and derivatives. Slopes can xy ≤ 3 also be utilized to construct linear relaxations for the 2 2 (2) x + y ≥ 1 problem. Figure 2 represents the DAG for Problem 2. 1 ≤ x ≤ 5 1 ≤ y ≤ 5 3 Interval arithmetic A factorable reformulation can proceed by introducing a new variable for every nonlinearity occurring in Interval arithmetic is a system of arithmetic based on 2 2 the model. We replace z1 for x , z2 for y and z3 for intervals of real numbers [137]. An interval variable xy. is defined using a variable’s lower and upper bounds; A factorable reformulation of the model is thus the variable itself is restricted to lie between the given by: bounds. Consider the interval variables ~x = [~xl, ~xu]

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