A comparative analysis of linear fitting for non-linear functions on optimization: A case study: Air pollution problems

A very frequent problem in advanced mathematical programming models is the linear approximation of convex and non-convex non-linear functions in either the constraints or the objective function of an otherwise linear programming problem. In this paper, based on a model that has been developed for the evaluation and selection of pollutant emission control policies and standards, we shall study several ways of representing non-linear functions of a single argument in mixed integer, separable and related programming terms. Thus we shall study the approximations based on piecewise constant, piecewise adjacent, piecewise non-adjacent additional and piecewise non-adjacent segmented functions. In each type of modelization we show the problem size and optimization results of using the following techniques: separable programming, mixed integer programming with Special Ordered Sets of type 1, linear programming with Special Ordered Sets of type 2 and mixed integer programming using strategies based on the quasi-integrality of the binary variables.     

Some branch and bound techniques for nonlinear optimization

The range of nonlinear optimization problems which can be solved by Linear Programming and the Branch and Bound algorithm is extended by introducing Chains of Linked Ordered Sets and by allowing automatic interpolation of new variables. However this approach involves solving a succession of linear subproblems, whose solutions in general violate the logical requirements of the nonlinear formulation and may lie far from any local or global optimum. The paper describes techniques which are designed to improve the performance of the Branch and Bound algorithm on problems containing chains, and which also yield benefits in integer programming.Each linear subproblem is tightened towards the corresponding nonlinear problem by removing variables which must logically be nonbasic in any feasible solution. This is achieved by a presolve procedure, and also by post-optimal Lagrangian relaxation which tightens the bound on the objective function by assessing the cheapest way to satisfy any violated chain constraints. Frequently fewer subsequent branches are required to find a feasible solution or to prove infeasibility.Formerly of Scicon Ltd.     

An algorithm for nonconvex programming problems

Branch and bound approaches for nonconvex programming problems had been given in [1] and [4]. Crucial for both are the use of rectangular partitions, convex envelopes and separable nonconvex portions of the objective function and constraints. We want to propose a similar algorithm which solves a sequence of problems in each of which the objective function is convex or even linear. The main difference between this approach and previous approaches is the use of general compact partitions instead of rectangular ones and a different refining rule such that the algorithm does not rely on the concept of convex envelopes and handles non-separable functions.First we describe a general algorithm and prove a convergence theorem under suitable regularity assumptions. Then we give as example an algorithm for concave minimization problems.     

Special ordered sets and an application to gas supply operations planning

Special Ordered Sets provide a powerful means of modeling nonconvex functions and discrete requirements, though there has been a tendency to think of them only in terms of multiple-choice zero-one programming. This paper emphasizes the origins and generality of the special ordered set concept, and describes an application in which type 2 sets are used in several forms to model both logical conditions and nonlinear functions.Now at IBM Almaden Research Center, San Jose, CA 95120.     

A note on the convergence of an algorithm for nonconvex programming problems

A strong convergence theorem is proven to hold for the general algorithm of the branch and bound type for solving nonconvex programming problems given in [1].     

AN EXTENSION OF PREDICTOR-CORRECTOR ALGORITHM TO A CLASS OF CONVEX SEPARABLE PROGRAM

1.IntroductionIn[1]Mizuno,ToddandYepresentedapredictor-correctoralgorithmforlinearpramgrammingwhichpossessesaquadraticconvergencerateofthedualgaptozero.GuoandWul6]gaveamodificationofthisalgorithmforsolvingconvexquadraticprogramwithupperbounds.Itisshownthatthemodifiedmethodnotonlypreservesalltheoriginalmerits,butalsoreducesthedualgapbyaconstantfactorineachcorrectorstep,incontrasttotheMizuno,TOddandYe'soriginalpredictor--correctormethodwherethedualgapremainsunchanged.Thealgorithmdiscussedint…     

Decomposition based and branch and bound global optimization approaches for the phase equilibrium problem

An increasingly popular approach when solving the phase and chemical equilibrium problem is to pose it as an optimization problem. However, difficulties are encountered due to the highly nonlinear nature of the models used to represent the behavior of the fluids, and because of the existence of multiple local solutions. This work shows how it is possible to guarantee -global solutions for a certain important class of the phase and chemical equilibrium problem, namely when the liquid phase can be modeled using neither the Non-Random Two-Liquid (NRTL) equation, or the UNIversal QUAsi Chemical (UNIQUAC) equation. Ideal vapor phases are easily incorporated into the global optimization framework. A numberof interesting properties are described which drastically alter the structure of the respective problems. For the NRTL equation, it is shown that the formulation can be converted into a biconvex optimization problem. The GOP algorithm of Floudas and Visweswaran [8, 9] can then be used to obtain -global solutions in this case. For the UNIQUAC equation, the new properties show how the objective function can be transformed into the difference of two convex functions (i.e. a D.C. programming problem is obtained), where the concave portion is separable. A branch and bound algorithm based on that of Falk and Soland [6] is used to guarantee convergence to an -global solution. Examples are presented which demonstrate the performance of both algorithms.     

Reverse Convex Programming Approach in the Space of Extreme Criteria for Optimization over Efficient Sets

The problem of minimizing a convex function over the difference of two convex sets is called ‘reverse convex program’. This is a typical problem in global optimization, in which local optima are in general different from global optima. Another typical example in global optimization is the optimization problem over the efficient set of a multiple criteria programming problem. In this article, we investigate some special cases of optimization problems over the efficient set, which can be transformed equivalently into reverse convex programs in the space of so-called extreme criteria of multiple criteria programming problems under consideration. A suitable algorithm of branch and bound type is then established for globally solving resulting problems. Preliminary computational results with the proposed algorithm are reported.     

Error bounds for convex differentiable inequality systems in Banach spaces

The paper is devoted to studying the Hoffman global error bound for convex quadratic/affine inequality/equality systems in the context of Banach spaces. We prove that the global error bound holds if the Hoffman local error bound is satisfied for each subsystem at some point of the solution set of the system under consideration. This result is applied to establishing the equivalence between the Hoffman error bound and the Abadie qualification condition, as well as a general version of Wang &; Pang's result [30], on error bound of Hölderian type. The results in the present paper generalize and unify recent works by Luo &; Luo in [17], Li in [16] and Wang &; Pang in [30].     

Stochastische dynamische Optimierung als Spezialfall linearer Optimierung in halbgeordneten Vektorräumen

In his papers [3], [4], [5], Krabs treats general linear programming problems in partially ordered vector spaces. Assuming the closedness of certain cones, he obtains duality and existence theorems. In the present paper, a linear programming problem fulfilling this assumption is derived. Furthermore, it is shown that dynamic programming problems can be treated by linear programming techniques even in the case of general state and action spaces.     

Global optimization algorithm for sum of generalized polynomial ratios problem

In this paper, a global optimization algorithm is proposed for solving sum of generalized polynomial ratios problem (P) which arises in various practical problems. Due to its intrinsic difficulty, less work has been devoted to globally solve the problem (P). For such problems, we present a branch and bound algorithm. In this method, by utilizing exponent transformation and new three-level linear relaxation method, a sequence of linear relaxation programming of the initial nonconvex programming problem (P) are derived which are embedded in a branch and bound algorithm. The proposed method need not introduce new variables and constraints and it is convergent to the global minimum of prime problem by means of the subsequent solutions of a series of linear programming problems. Several numerical examples in the literatures are tested to demonstrate that the proposed algorithm can systematically solve these examples to find the approximate ?-global optimum.     

Implementations of special ordered sets in MP software

Special ordered sets (SOS) have been introduced as a practical device for efficiently handling special classes of nonconvex optimization problems. They are now implemented in most commercial codes for mathematical programming (MP software). The paper gives a survey of possible applications as multiple choice restrictions, conditional multiple choice restrictions, discrete variables, discontinuous variables and piecewise linear functions, global optimization of separable programming problems, alternative right-hand sides, overlapping special ordered sets and the solution of quadratic programming problems. Alternative problem formulations are discussed. Since special ordered sets are not defined uniquely modelling facilities depend on the definition of a special orderedset in a code. The paper demonstrates the superiority of SOS to the application of binary variables if they are treated judiciously.     

解带有二次约束非凸二次规划问题的一个分枝缩减方法

在这篇论文里,有机地把外逼近方法与分枝定界技术结合起来,提出了解带有二次约束非凸二次规划问题的一个分枝缩减方法;给出了原问题的一个新的线性规划松弛,以便确定它在超矩形上全局最优值的一个下界;利用超矩形的一个深度二级剖分方法,以及超矩形的缩减和删除技术,提高算法的收敛速度;证明了在知道原问题可行点的条件下,该算法在有限步里就可以获得原问题的一个全局最优化解,并且用一个例子说明了该算法是有效的.     

B值随机元阵列的完全收敛性及大数定律

该文在随机元阵列随机有界于某非负随机变量的条件下，得到了B值随机元阵列完全收敛性的一般性结论，并讨论了随机元阵列加权和的收敛性，使[5][6]中的结果得到了改进和推广．同时讨论了完全收敛性与Banach空间p型（1＜P≤2）性质的等价性，使[14]，[15]中的结果得到进一步的改进．     

A branch and bound algorithm for globally solving a class of nonconvex programming problems

A branch and bound algorithm is proposed for globally solving a class of nonconvex programming problems (NP). For minimizing the problem, linear lower bounding functions (LLBFs) of objective function and constraint functions are constructed, then a relaxation linear programming is obtained which is solved by the simplex method and which provides the lower bound of the optimal value. The proposed algorithm is convergent to the global minimum through the successive refinement of linear relaxation of the feasible region and the solutions of a series of linear programming problems. And finally the numerical experiment is reported to show the feasibility and effectiveness of the proposed algorithm.     

Conical Algorithm in Global Optimization for Optimizing over Efficient Sets

The problem of optimizing some contiuous function over the efficient set of a multiple objective programming problem can be formulated as a nonconvex global optimization problem with special structure. Based on the conical branch and bound algorithm in global optimization, we establish an algorithm for optimizing over efficient sets and discuss about the implementation of this algorithm for some interesting special cases including the case of biobjective programming problems.     

Branch and bound algorithm for multidimensional scaling with city-block metric

A two level global optimization algorithm for multidimensional scaling (MDS) with city-block metric is proposed. The piecewise quadratic structure of the objective function is employed. At the upper level a combinatorial global optimization problem is solved by means of branch and bound method, where an objective function is defined as the minimum of a quadratic programming problem. The later is solved at the lower level by a standard quadratic programming algorithm. The proposed algorithm has been applied for auxiliary and practical problems whose global optimization counterpart was of dimensionality up to 24.     

Global optimization algorithm for a generalized linear multiplicative programming

This article present a branch and bound algorithm for globally solving generalized linear multiplicative programming problems with coefficients. The main computation involve solving a sequence of linear relaxation programming problems, and the algorithm economizes the required computations by conducting the branch and bound search in R ^p , rather than in R ⁿ , where p is the number of rank and n is the dimension of decision variables. The proposed algorithm will be convergent to the global optimal solution by means of the subsequent solutions of the series of linear relaxation programming problems. Numerical results are given to show the feasibility and effectiveness of the proposed algorithm.     

求线性比试和问题的确定性全局优化算法

为求线性比试和问题的全局最优解,本文给出了一个分支定界算法.通过一个等价问题和一个新的线性化松弛技巧,初始的非凸规划问题归结为一系列线性规划问题的求解.借助于这一系列线性规划问题的解,算法可收敛于初始非凸规划问题的最优解.算法的计算量主要是一些线性规划问题的求解.数值算例表明算法是切实可行的.     

Global optimization for sum of geometric fractional functions

This paper presents an efficient branch and bound algorithm for globally solving sum of geometric fractional functions under geometric constraints, which arise in various practical problems. By using an equivalent transformation and a new linear relaxation technique, a linear relaxation programming problem of the equivalent problem is obtained. The proposed algorithm is convergent to the global optimal solution by means of the subsequent solutions of a series of linear programming problems. Numerical results are reported to show the feasibility of our algorithm.