J
Jack Elzinga
Researcher at Johns Hopkins University
Publications - 6
Citations - 578
Jack Elzinga is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Minimax & Convex optimization. The author has an hindex of 5, co-authored 6 publications receiving 544 citations.
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Geometrical Solutions for Some Minimax Location Problems
Jack Elzinga,Donald W. Hearn +1 more
TL;DR: In this article, four closely related minimax location problems are considered, each involves locating a point in the plane to minimize the maximum distance (plus a possible constant) to a given finite set of points.
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A central cutting plane algorithm for the convex programming problem
Jack Elzinga,Thomas G. Moore +1 more
TL;DR: An algorithm is developed for solving the convex programming problem by constructing a cutting plane through the center of a polyhedral approximation to the optimum, which generates a sequence of primal feasible points whose limit points satisfy the Kuhn—Tucker conditions of the problem.
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Minimax Multifacility Location with Euclidean Distances
TL;DR: In this article, the problem of locating N new facilities among M existing facilities with the objective of minimizing the maximum weighed Euclidean distance among all facilities is considered. But the problem is not solved by maximizing a continuously differentiable concave objective subject to a small number of linear constraints.
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Letter to the Editor—A Note on a Minimax Location Problem
Jack Elzinga,Donald W. Hearn +1 more
TL;DR: In this article, a modification of Wesolowsky's approach was shown to result in a nonparametric linear program, and further, a different approach to the problem will yield a simpler formulation as a smaller linear program.
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The minimum sphere covering a convex polyhedron
Jack Elzinga,Donald W. Hearn +1 more
TL;DR: In this paper, a finite algorithm for finding the smallest sphere enclosing a convex polyhedron in En described by a given system of linear equalities or inequalities is given.