Nonmonotone algorithm for minimax optimization problems

Many real life problems can be stated as a minimax optimization problem, such as the problems in economics, finance, management, engineering and other fields. In this paper, we present an algorithm with nonmonotone strategy and second-order correction technique for minimax optimization problems. Using this scheme, the new algorithm can overcome the difficulties of the Maratos effect occurred in the nonsmooth optimization, and the global and superlinear convergence of the algorithm can be achieved accordingly. Numerical experiments indicate some advantages of this scheme.     

Relaxation-based algorithms for minimax optimization problems with resource allocation applications

We consider minimax optimization problems where each term in the objective function is a continuous, strictly decreasing function of a single variable and the constraints are linear. We develop relaxation-based algorithms to solve such problems. At each iteration, a relaxed minimax problem is solved, providing either an optimal solution or a better lower bound. We develop a general methodology for such relaxation schemes for the minimax optimization problem. The feasibility tests and formulation of subsequent relaxed problems can be done by using Phase I of the Simplex method and the Farkas multipliers provided by the final Simplex tableau when the corresponding problem is infeasible. Such relaxation-based algorithms are particularly attractive when the minimax optimization problem exhibits additional structure. We explore special structures for which the relaxed problem is formulated as a minimax problem with knapsack type constraints; efficient algorithms exist to solve such problems. The relaxation schemes are also adapted to solve certain resource allocation problems with substitutable resources. There, instead of Phase I of the Simplex method, a max-flow algorithm is used to test feasibility and formulate new relaxed problems.Corresponding author.Work was partially done while visiting AT&T Bell Laboratories.     

Minimax problems of discrete optimization invariant under majority operators

A special class of discrete optimization problems that are stated as a minimax modification of the constraint satisfaction problem is studied. The minimax formulation of the problem generalizes the classical problem to realistic situations where the constraints order the elements of the set by the degree of their feasibility, rather than defining a dichotomy between feasible and infeasible subsets. The invariance of this ordering under an operator is defined, and the discrete minimization of functions invariant under majority operators is proved to have polynomial complexity. A particular algorithm for this minimization is described.     

Strong duality for robust minimax fractional programming problems

We develop a duality theory for minimax fractional programming problems in the face of data uncertainty both in the objective and constraints. Following the framework of robust optimization, we establish strong duality between the robust counterpart of an uncertain minimax convex–concave fractional program, termed as robust minimax fractional program, and the optimistic counterpart of its uncertain conventional dual program, called optimistic dual. In the case of a robust minimax linear fractional program with scenario uncertainty in the numerator of the objective function, we show that the optimistic dual is a simple linear program when the constraint uncertainty is expressed as bounded intervals. We also show that the dual can be reformulated as a second-order cone programming problem when the constraint uncertainty is given by ellipsoids. In these cases, the optimistic dual problems are computationally tractable and their solutions can be validated in polynomial time. We further show that, for robust minimax linear fractional programs with interval uncertainty, the conventional dual of its robust counterpart and the optimistic dual are equivalent.     

非凸极小极大问题的优化算法与复杂度分析

非凸极小极大问题是近期国际上优化与机器学习、信号处理等交叉领域的一个重要研究前沿和热点,包括对抗学习、强化学习、分布式非凸优化等前沿研究方向的一些关键科学问题都归结为该类问题。国际上凸-凹极小极大问题的研究已取得很好的成果,但非凸极小极大问题不同于凸-凹极小极大问题,是有其自身结构的非凸非光滑优化问题,理论研究和求解难度都更具挑战性,一般都是NP-难的。重点介绍非凸极小极大问题的优化算法和复杂度分析方面的最新进展。     

Application of Bayesian approach to numerical methods of global and stochastic optimization

In this paper a review of application of Bayesian approach to global and stochastic optimization of continuous multimodal functions is given. Advantages and disadvantages of Bayesian approach (average case analysis), comparing it with more usual minimax approach (worst case analysis) are discussed. New interactive version of software for global optimization is discussed. Practical multidimensional problems of global optimization are considered     

A hybrid algorithm for linearly constrained minimax problems

Many real life problems can be stated as a minimax problem, such as economics, finance, management, engineering and other fields, which demonstrate the importance of having reliable methods to tackle minimax problems. In this paper, an algorithm for linearly constrained minimax problems is presented in which we combine the trust-region methods with the line-search methods and curve-search methods. By means of this hybrid technique, it avoids possibly solving the trust-region subproblems many times, and make better use of the advantages of different methods. Under weaker conditions, the global and superlinear convergence are achieved. Numerical experiments show that the new algorithm is robust and efficient.     

A global optimization algorithm for generalized semi-infinite,continuous minimax with coupled constraints and bi-level problems

We propose an algorithm for the global optimization of three problem classes: generalized semi-infinite, continuous coupled minimax and bi-level problems. We make no convexity assumptions. For each problem class, we construct an oracle that decides whether a given objective value is achievable or not. If a given value is achievable, the oracle returns a point with a value better than or equal to the target. A binary search is then performed until the global optimum is obtained with the desired accuracy. This is achieved by solving a series of appropriate finite minimax and min-max-min problems to global optimality. We use Laplace’s smoothing technique and a simulated annealing approach for the solution of these problems. We present computational examples for all three problem classes.     

A simplex social spider algorithm for solving integer programming and minimax problems

In this paper, we propose a new hybrid social spider algorithm with simplex Nelder-Mead method in order to solve integer programming and minimax problems. We call the proposed algorithm a Simplex Social Spider optimization (SSSO) algorithm. In the the proposed SSSO algorithm, we combine the social spider algorithm with its powerful capability of performing exploration, exploitation, and the Nelder-Mead method in order to refine the best obtained solution from the standard social spider algorithm. In order to investigate the general performance of the proposed SSSO algorithm, we test it on 7 integer programming problems and 10 minimax problems and compare against 10 algorithms for solving integer programming problems and 9 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.     

An Algorithm for the Global Optimization of a Class of Continuous Minimax Problems

We propose an algorithm for the global optimization of continuous minimax problems involving polynomials. The method can be described as a discretization approach to the well known semi-infinite formulation of the problem. We proceed by approximating the infinite number of constraints using tools and techniques from semidefinite programming. We then show that, under appropriate conditions, the SDP approximation converges to the globally optimal solution of the problem. We also discuss the numerical performance of the method on some test problems. Financial support of EPSRC Grant GR/T02560/01 gratefully acknowledged.     

On the lexicographic minimax approach to location problems

When locating public facilities, the distribution of travel distances among the service recipients is an important issue. It is usually tackled with the minimax (center) solution concept. The minimax solution concept, despite the most commonly used in the public sector location models, is criticized as it does not comply with the major principles of the efficiency and equity modeling. In this paper we develop a concept of the lexicographic minimax solution (lexicographic center) being a refinement of the standard minimax approach to location problems. We show that the lexicographic minimax approach complies with both the Pareto-optimality (efficiency) principle (crucial in multiple criteria optimization) and the principle of transfers (essential for equity measures) whereas the standard minimax approach may violate both these principles. Computational algorithms are developed for the lexicographic minimax solution of discrete location problems.     

Hyperbolic smoothing function method for minimax problems

In this article, an approach for solving finite minimax problems is proposed. This approach is based on the use of hyperbolic smoothing functions. In order to apply the hyperbolic smoothing we reformulate the objective function in the minimax problem and study the relationship between the original minimax and reformulated problems. We also study main properties of the hyperbolic smoothing function. Based on these results an algorithm for solving the finite minimax problem is proposed and this algorithm is implemented in general algebraic modelling system. We present preliminary results of numerical experiments with well-known nonsmooth optimization test problems. We also compare the proposed algorithm with the algorithm that uses the exponential smoothing function as well as with the algorithm based on nonlinear programming reformulation of the finite minimax problem.     

Mixed-integer minimax dynamic optimization for structure identification of glycerol metabolic network

Cell metabolism is a dynamic regulation process, in which its network structure and/or regulatory mechanisms can change constantly over time due to internal and external perturbations. This paper models glycerol metabolism in continuous fermentation as a nonlinear mixed-integer dynamic system by defining the time-varying metabolic network structure as an integer-valued function. To identify the dynamic network structure and kinetic parameters, we establish a mixed-integer minimax dynamic optimization problem with concentration robustness as its objective functional. By direct multiple shooting strategy and a decomposition approach consisting of convexification, relaxation and rounding strategy, the optimization problem is transformed into a large-scale approximate multistage parameter optimization problem. It is then solved using a competitive particle swarm optimization algorithm. We also show that the relaxation problem yields the best lower bound for the optimization problem, and its solution can be arbitrarily approximated by the solution obtained from rounding strategy. Numerical results indicate that the proposed mixed-integer dynamic system can better describe cellular self-regulation and response to intermediate metabolite inhibitions in continuous fermentation of glycerol. These numerical results show that the proposed numerical methods are effective in solving the large-scale mixed-integer dynamic optimization problems.     

Error bounds of two smoothing approximations for semi-infinite minimax problems

In the paper we investigate smoothing method for solving semi-infinite minimax problems. Not like most of the literature in semi-infinite minimax problems which are concerned with the continuous time version（i.e., the one dimensional semi-infinite minimax problems）, the primary focus of this paper is on multi- dimensional semi-infinite minimax problems. The global error bounds of two smoothing approximations for the objective function are given and compared. It is proved that the smoothing approximation given in this paper can provide a better error bound than the existing one in literature.     

An Algorithm Based on Active Sets and Smoothing for Discretized Semi-Infinite Minimax Problems

We present a new active-set strategy which can be used in conjunction with exponential (entropic) smoothing for solving large-scale minimax problems arising from the discretization of semi-infinite minimax problems. The main effect of the active-set strategy is to dramatically reduce the number of gradient calculations needed in the optimization. Discretization of multidimensional domains gives rise to minimax problems with thousands of component functions. We present an application to minimizing the sum of squares of the Lagrange polynomials to find good points for polynomial interpolation on the unit sphere in ℝ³. Our numerical results show that the active-set strategy results in a modified Armijo gradient or Gauss-Newton like methods requiring less than a quarter of the gradients, as compared to the use of these methods without our active-set strategy. Finally, we show how this strategy can be incorporated in an algorithm for solving semi-infinite minimax problems.     

Maximax and minimax rearrangement optimization problems

In this paper we present a general theory concerning two rearrangement optimization problems; one of maximization and the other of minimization type. The structure of the cost functional allows to formulate the two problems as maximax and minimax optimization problems. The latter proves to be far more interesting than the former. As an application of the theory we investigate a shape optimization problem which has already been addressed by other authors; however, here we prove our method is more efficient, and has the advantage that it captures more features of the optimal solutions than those obtained by others. The paper ends with a special case of the minimax problem, where we are able to obtain a minimum size estimate related to the optimal solution.     

求连续minimax问题整体解的区间算法

1 引 言 Minimax问题是一类重要的数学规划问题,它来源于实际并有极广泛的应用([1],[2]).用区间数学方法求解 minimax问题已取得了一些成果.文[1]对由 C2类函数构成的无约束连续 minimax问题进行了研究,建立了相应的区间算法,文[6]～[11]分别讨论和建立了无约束和不等式约束的离散minimax问题的区间算法.文[12]、[13]则讨论了最坏     

The relationship between the minimum-variance and minimax disparity RIM quantifier problems

Recently, Liu and Lou [On the equivalence of some approaches to the OWA operator and RIM quantifier determination, Fuzzy Sets and Systems 159 (2007) 1673-1688] investigated the equivalence of solutions to the minimum-variance and minimax disparity RIM quantifier problems. However, their proofs are very sensitive to the assumption, and some are mathematically incomplete. In this regard, this paper provides a counterexample of the minimax disparity RIM quantifier problem for the case in which generating functions are continuous. The paper also provides a correct proof of the minimax disparity RIM quantifier problem for the case in which generating functions are absolutely continuous and a generalized result for the minimum-variance RIM quantifier problem for the case in which generating functions are Lebesgue integrable. Based on the results, the paper provides a correct relationship between the minimum-variance and minimax disparity RIM quantifier problems.     

半无限极大极小问题的全局收敛方法

利用广义伪方向导数，在较弱的条件下，给出了半无限极大极小问题（P）的全局收敛性理论算法模型；利用离散策略给出了问题（P）全局收敛的可实现算法．数值结果表明本文给出的可实现算法是有效的．     

线性等式约束连续型minimax问题的区间算法

研究线性等式约束连续型minimax问题,其中目标函数为Lipschitz连续函数,基于线性约束函数的区间迭代运算、区域二分原则和无解区域删除原则,建立了求解线性等式约束连续型minimax问题的区间算法,证明了算法的相关定理,给出了数值算例,该算法保证求出问题的整体解,且是可靠和有效的.