Topic
Concave function
About: Concave function is a research topic. Over the lifetime, 1415 publications have been published within this topic receiving 33278 citations.
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TL;DR: In this paper, a sigmoid utility function was proposed to model risk sensitivity in Andean pastoralists, Ache foragers, and Sulawesi Crested Black Macaques.
24 citations
TL;DR: This paper considers the collection of convex programming problems with inequality and equality constraints, in which every problem of the collection is obtained by linear perturbations of the cost function and right-hand side perturbation of the constraints, while the ``core''cost function and the left- hand side constraint functions are kept fixed.
Abstract: In this paper we consider the collection of convex programming problems with inequality and equality constraints, in which every problem of the collection is obtained by linear perturbations of the cost function and right-hand side perturbation of the constraints, while the ``core'' cost function and the left-hand side constraint functions are kept fixed. The main result shows that the set of the problems which are not well-posed is σ-porous in a certain strong sense. Our results concern both the infinite and finite dimensional case. In the last case the conclusions are significantly sharper.
24 citations
TL;DR: An effective solution procedure, particularly useful for an approximation scheme, is proposed for the economic production quantity model with shortages under a general inventory cost rate function and piecewise linear concave production costs.
24 citations
TL;DR: This study constructs a discrete location model for profit maximization with endogenous consumer demands and network effects and develops an approximation algorithm based on demand function approximation, linear relaxation, decomposition, and sorting.
24 citations
TL;DR: Using linear underestimating approximations and their dual solutions, a combination of the proposed domain contraction via linear underestimation and its dual information with the branch and bound approach offers a computational strategy competitive with the currently dominant methods.
Abstract: Using linear underestimating approximations and their dual solutions, we develop new results for the nonconvex problem of minimizing a quadratic concave function under linear constraints. For solving this problem, which has such important management applications as economies of scale, fixed charge, and quadratic assignment problems, some results provide and tighten bounds on the global minimum value, while others identify the domains of the variables which may be excluded from further consideration. This leads to successive reduction of domains containing a global solution, called domain contraction, and ideally, to a solution with the objective value within some acceptable error level. For most results, specific problem instances are provided to establish their use in theory. In practice, experience with solving the problems presented here indicates that a combination of the proposed domain contraction via linear underestimation and its dual information with the branch and bound approach offers a computa...
24 citations