A simplified test for optimality

A simplification of recent characterizations of optimality in convex programming involving the cones of decrease and constancy of the objective and constraint functions is presented. In the original characterization due to Ben-Israelet al., optimality was verified or a feasible direction of decrease was determined by considering a number of sets equal to the number of subsets of the set of binding constraints. By first finding the set of constraints which is binding at every feasible point, it is possible to verify optimality or determine a feasible direction of decrease by considering a single set. In the case of faithfully convex functions, this set can be found by solving at mostp systems of linear equations and inequalities, wherep is the number of constraints.This work was partly supported by NSF Grant No. Eng 76-10260.     

Stochastic algorithms with armijo stepsizes for minimization of functions

Stochastic algorithms for optimization problems, where function evaluations are done by Monte Carlo simulations, are presented. At each iteratex _i, they draw a predetermined numbern(i) of sample points from an underlying probability space; based on these sample points, they compute a feasible-descent direction, an Armijo stepsize, and the next iteratex _i+1. For an appropriate optimality function , corresponding to an optimality condition, it is shown that, ifn(i) , then (x _i) 0, whereJ is a set of integers whose upper density is zero. First, convergence is shown for a general algorithm prototype: then, a steepest-descent algorithm for unconstrained problems and a feasible-direction algorithm for problems with inequality constraints are developed. A numerical example is supplied.     

Tradeoff directions in multiobjective optimization problems

Multiobjective optimization problems (MOP) typically have conflicting objectives wherein a gain in one objective is at the expense of another. Tradeoff directions, which measure the change in some objectives relative to changes in others, provide important information as to the best direction of improvement from the current solution. In this paper we present a general definition of tradeoffs as a cone of directions and provide a general method of calculating tradeoffs at every Pareto optimal point of a convex MOP. This extends current definitions of tradeoffs which assume certain conditions on the feasible set and the objective functions. Two comprehensive numerical examples are provided to illustrate the tradeoff directions and the methods used to calculate them.     

An algorithm for the minimax weber problem

The paper presents an algorithm for finding the weighted absolute center of a given set of points in the Euclidean plane.The algorithm is a specialized implementation of the method of feasible directions.The presentation includes a proof of convergence as well as computational experience.     

On optimality conditions for maximizations with respect to cones

For a Pareto maximization problem defined in infinite dimensions in terms of cones, relationships among several types of maximal elements are noted and optimality conditions are developed in terms of tangent cones.     

Duality theorem of nondifferentiable convex multiobjective programming

Necessary and sufficient conditions of Fritz John type for Pareto optimality of multiobjective programming problems are derived. This article suggests to establish a Wolfe-type duality theorem for nonlinear, nondifferentiable, convex multiobjective minimization problems. The vector Lagrangian and the generalized saddle point for Pareto optimality are studied. Some previously known results are shown to be special cases of the results described in this paper.This research was partly supported by the National Science Council, Taipei, ROC.The authors would like to thank the two referees for their valuable suggestions on the original draft.     

The limiting Lagrangian as a consequence of Helly's theorem

The perturbational Lagrangian equation established by Jeroslow in convex semi-infinite programming is derived from Helly's theorem and some prior results on one-dimensional perturbations of convex programs.This research was partially supported by NRC, Grant No. A-4493.     

Solutions of Fuzzy Multiobjective Programming Problems Based on the Concept of Scalarization

Scalarization of fuzzy multiobjective programming problems using the embedding theorem and the concept of convex cone (ordering cone) is proposed in this paper. Since the set of all fuzzy numbers can be embedded into a normed space, this motivation naturally inspires us to invoke the scalarization techniques in vector optimization problems to evaluate the a multiobjective programming problem. Two solution concepts are proposed in this paper by considering different convex cones.     

Weak Pareto optimality of multiobjective problems in a locally convex linear topological space

In this study, the Dubovitskii-Milyutin type optimization theory is extended to multiobjective programs in a locally convex linear topological space, producing necessary conditions for a weak Pareto optimum. In the case of an ordinary multiobjective convex program, generalized Kuhn-Tucker conditions by a subdifferential formula are necessary and sufficient for a weak Pareto optimum.The author is grateful for the useful suggestions and comments of Professor N. Furukawa and the referee.     

Weak Pareto optimality of multiobjective problems in a locally convex linear topological space

Several corrections to Ref. 1 are pointed out.     

On the existence and multiplicity of positive periodic solutions for first-order vector differential equation

In this paper, we consider the existence and multiplicity of positive periodic solutions for first-order vector differential equation x^′(t)+f(t,x(t))=0, a.e. t∈[0,ω] under the periodic boundary value condition x(0)=x(ω). Here ω is a positive constant, and is a Carathéodory function. Some existence and multiplicity results of positive periodic solutions are derived by using a fixed point theorem in cones.     

A modified Newton method for minimization

Some promising ideas for minimizing a nonlinear function, whose first and second derivatives are given, by a modified Newton method, were introduced by Fiacco and McCormick (Ref. 1). Unfortunately, in developing a method around these ideas, Fiacco and McCormick used a potentially unstable, or even impossible, matrix factorization. Using some recently developed techniques for factorizing an indefinite symmetric matrix, we are able to produce a method which is similar to Fiacco and McCormick's original method, but avoids the difficulties of the original method.Both authors gratefully acknowledge the award of a research fellowship from the British Science Research Council.     

变量有广义界线性规划的直接对偶单纯形法

本文讨论变量有广义界线性规划问题借助标准形线性规划同单纯形法技术，建立问题的一个直接对偶单纯形法。分析了方法的性质，给出了初始对偶可行基的计算方法，并用实例说明方法的具体操作。     

Optimal scheduling of reservoir releases during flood: Deterministic optimization problem,part 1, procedure

In this paper, we are concerned with the optimal scheduling of water releases from retention reservoirs during flood, with the objective of minimizing flood damages at the important damage centers downstream of the reservoirs. Unlike in most other papers devoted to this subject, the flood routing equations are nonlinear. The performance index of the problem leads to a minimax optimal control problem. For this problem, the necessary optimality conditions are provided and a version of the feasible directions method is proposed.The research reported here has been supported by the Central Basic Research Program CPBP-03.09, Metody Analizy i Uytkowania Zasobow Wodnych, Polish Academy of Sciences, Warsaw, Poland. This support is kindly acknowledged.     

Non-causal models in long term planning via set contractive optimal control methods

The notion of consistency for optimal plans introduced in [F.E. Kydland, E.C. Prescott, Rules rather than decisions: The inconsistency of optimal plans, J. Polit. Econ. 85 (3) (1977) 473–491] is studied in relation to dynamic programming and to multi-objective optimal control. It is demonstrated that the consistency condition for an optimal sequence of n policies for periods from 1 to n corresponding to a sequence of n economic agents’ decisions that together constitute an optimal plan for an agreed-upon social objective function is equivalent to a multi-objective problem with n+1 criteria. An optimal plan is consistent if and only if the corresponding (n+1)-criteria problem is balanced, and consistent optimal plans do exist in such economic situations. Otherwise, the consistency requirement generates the balance set and a set of Pareto solutions, and both can be computed as illustrated in the inflation–unemployment example extended for two periods with three objectives to optimize. The procedure of “consistent planning for the infinite horizon” considered in [F.E. Kydland, E.C. Prescott, Rules rather than decisions: The inconsistency of optimal plans, J. Polit. Econ. 85 (3) (1977) 473–491] is shown to concur with Bellman’s principle of optimality that may be invalid in many practical cases including the case considered in [F.E. Kydland, E.C. Prescott, Rules rather than decisions: The inconsistency of optimal plans, J. Polit. Econ. 85 (3) (1977) 473–491]. Finitely causal and non-causal models are introduced for long term planning in a multi-objective (Pareto) framework, and it is demonstrated how to deal with non-causal models using new set-contractive methods for multi-objective global optimization. The results are illustrated by examples.     

A uniform boundedness theorem for locally convex cones

We prove a uniform boundedness theorem for families of linear operators on ordered cones. Using the concept of locally convex cones we introduce the notions of barreled cones and of weak cone-completeness. Our main result, though no straightforward generalization of the classical case, implies the Uniform Boundedness Theorem for Fréchet spaces.

     

Existence of sign-changing solutions for the p-Laplacian equation from linking type theorem

In this paper, the existence of sign-changing solutions for p-Laplacian equation is obtained via a new linking theorem.     

A Solution Concept for Fuzzy Multiobjective Programming Problems Based on Convex Cones

A solution concept for fuzzy multiobjective programming problems based on ordering cones (convex cones) is proposed in this paper. The notions of ordering cones and partial orderings on a vector space are essentially equivalent. Therefore, the optimality notions in a real vector space can be elicited naturally by invoking a concept similar to that of the Pareto-optimal solution in vector optimization problems. We introduce a corresponding multiobjective programming problem and a weighting problem of the original fuzzy multiobjective programming problem using linear functionals so that the optimal solution of its corresponding weighting problem is also the Pareto-optimal solution of the original fuzzy multiobjective programming problem.