This paper proposes a method for estimating enrollment transition rates in countries where repetition counts are nonexistent or unreliable. The method, based on linear programming, requires a minimum of data and allow...
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In this paper, we investigate model-independent bounds for option prices given a set of market instruments. This super-replication problem can be written as a semi-infinite linear programing problem. As these super-re...
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In this paper, we investigate model-independent bounds for option prices given a set of market instruments. This super-replication problem can be written as a semi-infinite linear programing problem. As these super-replication prices can be large and the densities Q which achieve the upper bounds quite singular, we restrict Q to be close in the entropy sense to a prior probability measure at a next stage. This leads to our risk-neutral weighted Monte Carlo approach which is connected to a constrained convex problem. We explain how to solve efficiently these large-scale problems using a primal-dual interiorpoint algorithm within the cutting-plane method and a quasi-Newton algorithm. Various examples illustrate the efficiency of these algorithms and the large range of applicability.
linear programing was used to plan the desegregation program of the Seattle (WA) School District. The application described is unique in two respects: (1) its nearest/next nearest school distinction, using geographica...
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linear programing was used to plan the desegregation program of the Seattle (WA) School District. The application described is unique in two respects: (1) its nearest/next nearest school distinction, using geographically coded student files, and (2) its measurement of the compatibility of two alternative legal frameworks for achieving racial balance. (Author/BW)
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