Finite Minimax Problems Research Articles

An entropic regularized method of centers for continuous minimax problem with semi infinite constraints

The purpose of this paper is to focus on continuous minimax problems with semi infinite constraints. We begin by proposing an algorithm for solving this kind of problems. The proposed algorithm combines the parametric approach and the Huard method of centers. That’s we extend the Method of Centers of Roubi for solving minimax fractional programs to continuous minimax problems. On the other hand, calculating the exact optimal solution of the proposed parametric problems, requires an extraordinary computational work per iteration. To overcome this problem we use an iterative entropic regularization method which allows us to solve each auxiliary problem inexactly generating an approximate sequence of optimal values and then the continuous minimax problem is reduced into a sequence of finite minimax problems. Examples for illustration are given to test our algorithm.

A new three-term conjugate gradient method for solving the finite minimax problems

In this paper, we consider the method for solving the finite minimax problems. By using the exponential penalty function to smooth the finite minimax problems, a new three-term nonlinear conjugate gradient method is proposed for solving the finite minimax problems, which generates sufficient descent direction at each iteration. Under standard assumptions, the global convergence of the proposed new three-term nonlinear conjugate gradient method with Armijo-type line search is established. Numerical results are given to illustrate that the proposed method can efficiently solve several kinds of optimization problems, including the finite minimax problem, the finite minimax problem with tensor structure, the constrained optimization problem and the constrained optimization problem with tensor structure.

Solution of network localization problem with noisy distances and its convergence

The network localization problem with convex and non-convex distance constraints may be modeled as a nonlinear optimization problem. In the existing localization techniques, the non-convex distance constraints are either removed or relaxed into convex constraints to use the convex optimization methods like semi-definite programming, least square approximation, etc. for solving the problem. We use the nonlinear Lagrangian technique for non-convex optimization to convert the localization problem with noisy distance measurements to a root finding problem of a single variable continuous function without any modification of these constraints. This problem is then solved using an iterative method. In this iterative method, the computation of the functional values involves a finite mini-max problem. We use smoothing gradient method to compute the functional value. The proposed iterative method converges to a solution of the network localization problem with a desired label of accuracy in real time.

A smoothing iterative method for the finite minimax problem

In this paper, we proposed a smoothing iterative method to solve the finite minimax problems based on the exponential penalty function of Kort and Bertsekas (1972). This approach can be viewed as the extension of one conjugate gradient method. Under suitable conditions, the proposed method is globally convergent. Preliminary numerical results and comparisons show that the proposed method is effective and promising.

Network localisation using Lagrangian optimisation

The network localisation problem with non-convex distance constraints may be modelled as a nonlinear optimisation problem. The existing localisation techniques either eliminate the nonconvex constraints or relax them into convex constraints to employ the traditional convex optimisations like semi-definite programming (SDP), least square approximation, etc. to find the node positions. We propose a method to solve the original network localisation problem with noisy distance measurements without any modification of non-convex constraints. Using the nonlinear Lagrangian technique for non-convex optimisation, we convert the localisation problem to a root finding problem involving single variable. This problem is then solved using bisection method. For computing functional values, it involves finite mini-max problem (FMX). We use sequential quadratic programming (SQP) to fix the FMX problem. Simulations studies show that, the number of iterations in the proposed method is reasonable to achieve any desired label of accuracy in node positions.

An active-set algorithm and a trust-region approach in constrained minimax problem

In this paper, we propose an algorithm to solve a finite minimax problem with side constraints. An active-set strategy is used in the algorithm to transform inequality constraints to equality constraints. This allows the use of the well-developed techniques for solving the equality constrained optimization problems. A trust-region globalization strategy is added to the proposed algorithm to ensure global convergence. A projected Hessian technique is used in the algorithm to overcome the difficulty of having an infeasible trust-region subproblem. A global convergence theory for the proposed algorithm is presented under standard assumptions. Finally, numerical experiments are reported to indicate that the new algorithm performs efficiently in practice.

A spline smoothing Newton method for finite minimax problems

A spline smoothing stabilized Newton method for finite minimax problems is developed. The spline smoothing technique uses a smooth cubic spline instead of a max function, and, at any fixed point, only a few components of the max function are involved; i.e., it introduces an active set technique, so the proposed method is more efficient for minimizing the maximum function of a large number of complicated functions. Some numerical results comparisons with other methods are also given to show the efficiency of the new method.

Improved Filter-SQP Algorithm with Active Set for Constrained Minimax Problems

An improved filter-SQP algorithm with active set for constrained finite minimax problems is proposed. Firstly, an active constraint subset is obtained by a pivoting operation procedure. Then, a new quadratic programming (QP) subproblem is constructed based on the active constraint subset. The main search directiondkis obtained by solving this (QP) subproblem which is feasible at per iteration point and need not to consider the penalty function by using the filter technique. Under some suitable conditions, the global convergence of our algorithm is established. Finally, some numerical results are reported to show the effectiveness of the proposed algorithm.

A Simple SQP Algorithm for Constrained Finite Minimax Problems

A simple sequential quadratic programming method is proposed to solve the constrained minimax problem. At each iteration, through introducing an auxiliary variable, the descent direction is given by solving only one quadratic programming. By solving a corresponding quadratic programming, a high-order revised direction is obtained, which can avoid the Maratos effect. Furthermore, under some mild conditions, the global and superlinear convergence of the algorithm is achieved. Finally, some numerical results reported show that the algorithm in this paper is successful.

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.

Derivative-free optimization methods for finite minimax problems

Derivative-free optimization focuses on designing methods to solve optimization problems without the analytical knowledge of the function. In this paper, we consider the problem of designing derivative-free methods for finite minimax problems: min x max i=1, 2, …, N <texlscub>f i (x)</texlscub>. In order to solve the problem efficiently, we seek to exploit the smooth substructure within the problem. Using ideas developed by Burke et al. [J.V. Burke, A.S. Lewis, and M.L. Overton, Approximating subdifferentials by random sampling of gradients, Math. Oper. Res. 27(3) (2002), pp. 567–584; J.V. Burke, A.S. Lewis, and M.L. Overton, A robust gradient sampling algorithm for nonsmooth, nonconvex optimization, SIAM J. Optim. 15(3) (2005), pp. 751–779 (electronic)], we create the idea of a robust simplex gradient descent direction and use it to accelerate convergence. Convergence is proven by showing that the resulting algorithm fits into the directional direct-search framework. Numerical tests demonstrate the algorithm's effectiveness on finite minimax problems.

A derivative-free approximate gradient sampling algorithm for finite minimax problems

In this paper we present a derivative-free optimization algorithm for finite minimax problems. The algorithm calculates an approximate gradient for each of the active functions of the finite max function and uses these to generate an approximate subdifferential. The negative projection of 0 onto this set is used as a descent direction in an Armijo-like line search. We also present a robust version of the algorithm, which uses the ‘almost active’ functions of the finite max function in the calculation of the approximate subdifferential. Convergence results are presented for both algorithms, showing that either f(x k)→−∞ or every cluster point is a Clarke stationary point. Theoretical and numerical results are presented for three specific approximate gradients: the simplex gradient, the centered simplex gradient and the Gupal estimate of the gradient of the Steklov averaged function. A performance comparison is made between the regular and robust algorithms, the three approximate gradients, and a regular and robust stopping condition.

On Solving Large-Scale Finite Minimax Problems Using Exponential Smoothing

This paper focuses on finite minimax problems with many functions, and their solution by means of exponential smoothing. We conduct run-time complexity and rate of convergence analysis of smoothing algorithms and compare them with those of SQP algorithms. We find that smoothing algorithms may have only sublinear rate of convergence, but as shown by our complexity results, their slow rate of convergence may be compensated by small computational work per iteration. We present two smoothing algorithms with active-set strategies and novel precision-parameter adjustment schemes. Numerical results indicate that the algorithms are competitive with other algorithms from the literature, and especially so when a large number of functions are nearly active at stationary points.

Pattern search methods for finite minimax problems

In this paper, we propose pattern search methods for finite minimax problems. Due to the nonsmoothness of this class of problems, we convert the original problem into a smooth one by using a smoothing technique based on the exponential penalty function of Kort and Bertsekas, which technique depends on a smoothing parameter that control the approximation to the finite minimax problems. The proposed methods are based on a sampling of the smooth function along a set of suitable search directions and on an updating rule for the step-control parameter. Under suitable conditions, we get the global convergence results despite the fact that pattern search methods do not have explicit information concerning the gradient and consequently are unable to enforce explicitly a notion of sufficient feasible decrease.

A hybrid algorithm for nonlinear minimax problems

In this paper, a hybrid algorithm for solving finite minimax problem is presented. In the algorithm, we combine the trust-region methods with the line-search methods and curve-search methods. By means of this hybrid technique, the algorithm, according to the specific situation at each iteration, can adaptively performs the trust-region step, line-search step or curve-search step, so as to avoid possibly solving the trust-region subproblems many times, and make better use of the advantages of different methods. Moreover, we use second-order correction step to circumvent the difficulties of the Maratos effect occurred in the nonsmooth optimization. Under mild conditions, we prove that the new algorithm is of global convergence and locally superlinear convergence. The preliminary experiments show that the new algorithm performs efficiently.

A derivative-free algorithm for systems of nonlinear inequalities

Recently a new derivative-free algorithm has been proposed for the solution of linearly constrained finite minimax problems. This derivative-free algorithm is based on a smoothing technique that allows one to take into account the non-smoothness of the max function. In this paper, we investigate, both from a theoretical and computational point of view, the behavior of the minmax algorithm when used to solve systems of nonlinear inequalities when derivatives are unavailable. In particular, we show an interesting property of the algorithm, namely, under some mild conditions regarding the regularity of the functions defining the system, it is possible to prove that the algorithm locates a solution of the problem after a finite number of iterations. Furthermore, under a weaker regularity condition, it is possible to show that an accumulation point of the sequence generated by the algorithm exists which is a solution of the system. Moreover, we carried out numerical experimentation and comparison of the method against a standard pattern search minimization method. The obtained results confirm that the good theoretical properties of the method correspond to interesting numerical performance. Moreover, the algorithm compares favorably with a standard derivative-free method, and this seems to indicate that extending the smoothing technique to pattern search algorithms can be beneficial.

A smoothing trust-region Newton-CG method for minimax problem

This paper presents a smooth approximate method with a new smoothing technique and a standard unconstrained minimization algorithm in the solution to the finite minimax problems. The new smooth approximations only replace the original problem in some neighborhoods of the kink points with a twice continuously differentiable function, its gradient and Hessian matrix are the combination of the first and the second order derivative of the original functions respectively. Compared to the other smooth functions such as the exponential penalty function, the remarkable advantage of the new smooth function is that the combination coefficients of its gradient and the Hessian matrix have sparse properties. Furthermore, the maximal possible difference value between the optimal values of the smooth approximate problem and the original one is determined by a fixed parameter selected previous. An algorithm to solve the equivalent unconstrained problem by using the trust-region Newton conjugate gradient method is proposed in the solution process. Finally, some numerical examples are reported to compare the proposed algorithm with SQP algorithm that implements in MATLAB toolbox and the algorithm in [E. Polak, J.O. Royset, R.S. Womersley, Algorithms with adaptive smoothing for finite minimax problems, Journal of Optimization Theory and Applications 119 (3) (2003) 459–484] based on the exponential penalty function, the numerical results prove that the proved algorithm is efficient.

A Derivative-Free Algorithm for Linearly Constrained Finite Minimax Problems

In this paper we propose a new derivative-free algorithm for linearly constrained finite minimax problems. Due to the nonsmoothness of this class of problems, standard derivative-free algorithms can locate only points which satisfy weak necessary optimality conditions. In this work we define a new derivative-free algorithm which is globally convergent toward standard stationary points of the finite minimax problem. To this end, we convert the original problem into a smooth one by using a smoothing technique based on the exponential penalty function of Kort and Bertsekas. This technique depends on a smoothing parameter which controls the approximation to the finite minimax problem. The proposed method is based on a sampling of the smooth function along a suitable search direction and on a particular updating rule for the smoothing parameter that depends on the sampling stepsize. Numerical results on a set of standard minimax test problems are reported.

Algorithms with Adaptive Smoothing for Finite Minimax Problems

We present a new feedback precision-adjustment rule for use with a smoothing technique and standard unconstrained minimization algorithms in the solution of finite minimax problems. Initially, the feedback rule keeps a precision parameter low, but allows it to grow as the number of iterations of the resulting algorithm goes to infinity. Consequently, the ill-conditioning usually associated with large precision parameters is considerably reduced, resulting in more efficient solution of finite minimax problems.

A parametric embedding for the finite minimax problem

We consider unconstrained finite minimax problems where the objective function is described as a maximum of functions fk∈C3(ℜn,ℜ). We propose a parametric embedding for the minimax problem and, assuming that the corresponding parametric optimization problem belongs to the generic class of Jongen, Jonker and Twilt, we show that if one applies pathfollowing methods (with jumps) to the embedding in the convex case (in the nonconvex case) one obtains globally convergent algorithms. Furthermore, we prove under usual assumptions on the minimax problem that pathfollowing methods applied to a perturbed parametric embedding of the original minimax problem yield globally convergent algorithms for almost all perturbations.