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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 49 (1986), S. 319-337 
    ISSN: 1573-2878
    Keywords: Efficient sets ; ε-efficiency ; weighting factors ; constrained objectives ; penalty functions ; ideal points ; Markov decision processes
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper considers the extension of ε-optimality for scalar problems to vector maximization problems, or efficiency problems, which havem objective functions defined on a set $$X \subseteq \mathbb{R}^n $$ . It is shown that the natural extension of the scalar ε-optimality concepts [viz, given ε〉0, given a solution setS, ifx∈S there exists an efficient solutiony with ∥f(x)−f(y)∥≦ε, and given an efficient solutiony, there exists anx∈S with ∥f(x)−f(y)∥≦ε] do not hold for some methods used. Six concepts of ε-efficient sets are introduced and examined, to a very limited extent, in the context of five methods used for generating efficient points or near efficient points. In doing so, a distinction is drawn between methods in which the surrogate optimizations are carried out exactly, and those where terminal ε-optimal solutions are obtained.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 43 (1984), S. 583-599 
    ISSN: 1573-2878
    Keywords: Efficient sets ; penalty functions ; point convergence ; set convergence ; limit sets
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper generalizes the penalty function method of Zang-will for scalar problems to vector problems. The vector penalty function takes the form $$g(x,\lambda ) = f(x) + \lambda ^{ - 1} P(x)e,$$ wheree ⊀R m, with each component equal to unity;f:R n →R m, represents them objective functions {f i} defined onX $$ \subseteq $$ R n; λ ∈R 1, λ〉0;P:R n →R 1 X $$ \subseteq $$ Z $$ \subseteq $$ R n,P(x)≦0, ∨x ∈R n,P(x) = 0 ⇄x ∈X. The paper studies properties of {E (Z, λ r )} for a sequence of positive {λ r } converging to 0 in relationship toE(X), whereE(Z, λ r ) is the efficient set ofZ with respect tog(·, λr) andE(X) is the efficient set ofX with respect tof. It is seen that some of Zangwill's results do not hold for the vector problem. In addition, some new results are given.
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 93 (1997), S. 183-197 
    ISSN: 1573-2878
    Keywords: Trilevel programming ; penalty functions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In this paper, we study a trilevel decision-making situation in which decisionmaker 1 selects an action, within a specified constraint set, then decisionmaker 2 selects an action within a constraint set determined by the action of decisionmaker 2, and finally decisionmaker 3 selects an action within a constraint set determined by the actions of decisionmakers 1 and 2. Each decisionmaker has an objective function to be optimized within the imposed constraint set. Bard (Ref. 1) presents a five-step algorithm for solving this problem. This paper presents an alternative approach to the key first step of that algorithm, which has some qualitative advantages over it.
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    OR spectrum 15 (1994), S. 225-230 
    ISSN: 1436-6304
    Keywords: Markov decision processes ; ɛ-optimal ; variance ; Markov'sche Entscheidungsprozesse ; ɛ-Optimalität ; Gewinnvarianz
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Description / Table of Contents: Zusammenfassung In dieser Arbeit geben wir drei Algorithmen zur Lösung eines stochastischen dynamischen Problems an. Dabei wird ein optimaler Ausgleich zwischen erwartetem Gewinn und Gewinnvarianz pro Zeiteinheit gesucht. Die Algorithmen liefern für beliebigesɛ〉0 jeweilsɛ-optimale Lösungen.
    Notes: Abstract In this paper we present three algorithms for solving a problem in which it is required to get an optimal compromise between the average expected reward per unit time and the variance of the reward per unit time. The algorithms lead to anɛ-optimal solution, whereɛ〉0 is arbitrary.
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