“…In fact, as noted by Geahart (1979), his algorithm will work even if we assume a substantially weaker property than convexity. Indeed, it is sufficient that for one of the criterion components every local minimum is a global minimum, whereas the other component is upper semicontinuous (see Theorem 3.3 in Geahart, 1979).…”
Section: Convexity and Differentiability Of Controlled Risk 303mentioning
confidence: 95%
“…Indeed, it is sufficient that for one of the criterion components every local minimum is a global minimum, whereas the other component is upper semicontinuous (see Theorem 3.3 in Geahart, 1979).…”
Section: Convexity and Differentiability Of Controlled Risk 303mentioning
confidence: 98%
“…We mention two algorithms that have been specially developed for problems with a two-component criterion (Geahart, 1979;Paine et al, 1975). The algorithm of Paine et al (1975) is designed for problems with continuously differentiable, but not necessarily convex functions; the algorithm of Geahart (1979) is designed for problems that have convexity properties, but are not necessarily differentiable.…”
Section: Constraintmentioning
confidence: 99%
“…We focus on two algorithms especially developed for problems with a two-dimensional criterion (Geahart, 1979;Paine et al, 1975). Geahart's (1979) algorithm utilizes the convexity properties of the problem, whereas the algorithm of Paine et al (1975) assumes continuous differentiability of the functions. Both algorithms produce Pareto-optimal solutions.…”
Section: Convexity and Differentiability Of Controlled Risk 303mentioning
We investigate risk associated with the violation of a constraint, which is desirable but hardly satisfiable in all possible states of nature. Copyright Springer 2004Controlled risk, Violation of Constraints, convexity, differentiability,
“…In fact, as noted by Geahart (1979), his algorithm will work even if we assume a substantially weaker property than convexity. Indeed, it is sufficient that for one of the criterion components every local minimum is a global minimum, whereas the other component is upper semicontinuous (see Theorem 3.3 in Geahart, 1979).…”
Section: Convexity and Differentiability Of Controlled Risk 303mentioning
confidence: 95%
“…Indeed, it is sufficient that for one of the criterion components every local minimum is a global minimum, whereas the other component is upper semicontinuous (see Theorem 3.3 in Geahart, 1979).…”
Section: Convexity and Differentiability Of Controlled Risk 303mentioning
confidence: 98%
“…We mention two algorithms that have been specially developed for problems with a two-component criterion (Geahart, 1979;Paine et al, 1975). The algorithm of Paine et al (1975) is designed for problems with continuously differentiable, but not necessarily convex functions; the algorithm of Geahart (1979) is designed for problems that have convexity properties, but are not necessarily differentiable.…”
Section: Constraintmentioning
confidence: 99%
“…We focus on two algorithms especially developed for problems with a two-dimensional criterion (Geahart, 1979;Paine et al, 1975). Geahart's (1979) algorithm utilizes the convexity properties of the problem, whereas the algorithm of Paine et al (1975) assumes continuous differentiability of the functions. Both algorithms produce Pareto-optimal solutions.…”
Section: Convexity and Differentiability Of Controlled Risk 303mentioning
We investigate risk associated with the violation of a constraint, which is desirable but hardly satisfiable in all possible states of nature. Copyright Springer 2004Controlled risk, Violation of Constraints, convexity, differentiability,
“…Theoretical research on bicriteria optimization problems, initiated perhaps by Geoffrion (1967), was conducted by Pasternak and Passy (1 973), Benson (1979), Gearhart (1979) and others. Payne et al (1975) were among the first researchers to propose algorithms for solving bicriteria problems.…”
In multiple-objective programming, a knowledge of the structure of the non-dominated set can aid in generating efficient solutions. We present new concepts which allow for a better understanding of the structure of the set of nondominated solutions for non-convex bicriteria programming problems. In particular, a means of determining whether or not this set is connected is examined. Both supersets and newly defined subsets of the non-dominated set are utilized in this investigation. Of additional value is the use of the lower envelope of the set of outcomes in classifying feasible points as (properly) non-dominated solutions.
In this article a bicriteria model, formed by the weighted sum of the minisum and minimax functions for a single‐location problem, is investigated. It is shown that all efficient solutions generated by either constrained model are also properly efficient. The bicriteria model and the constrained models are theoretically equivalent, but it is more efficient and simpler to generate nondominated solutions using the constrained criterion approach. When solving the bicriteria model, a critical range is found for which all properly efficient solutions are generated.
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