首页|Linear, nested, and quadratic ordered measures: Computation and
incorporation into optimization problems
Linear, nested, and quadratic ordered measures: Computation and incorporation into optimization problems
Linear, nested, and quadratic ordered measures: Computation and incorporation into optimization problems
数学计算技术、计算机技术
Victor Blanco,Miguel A. Pozo,Justo Puerto,Alberto Torrejon.Linear, nested, and quadratic ordered measures: Computation and incorporation into optimization problems[EB/OL].(2025-03-23)[2025-10-25].https://arxiv.org/abs/2503.18097.点此复制
In this paper we address a unified mathematical optimization framework to
compute a wide range of measures used in most operations research and data
science contexts. The goal is to embed such metrics within general optimization
models allowing their efficient computation. We assess the usefulness of this
approach applying it to three different families of measures, namely linear,
nested, and quadratic ordered measures. Computational results are reported
showing the efficiency and accuracy of our methods as compared with standard
implementations in numerical software packages. Finally, we illustrate this
methodology by computing a number of optimal solutions with respect to
different metrics on three well-known linear and combinatorial optimization
problems: scenario analysis in linear programming, the traveling salesman and
the weighted multicover set problem.
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