which can be transformed to a linear interval programming model studied in the paper. The method is illustrated by solving a simplified portfolio selection problem.

Abstract: The sparsity-regularized linear inverse problem has been widely used in many fields, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed sensing, ...

PaPILO, a C++14-based software package, provides parallel presolve routines for (mixed integer) linear programming problems. The routines are implemented ... If the presolve method needs to add ...

This article presents a numerical model based on the finite difference method for the physical and geometric non-linear analysis of a one-dimensional consolidation problem regarding a saturated, ...

These experiments demonstrate the effectiveness of the method and its competitiveness when compared to previous works. The assignment problem (AP) aims to find an assignment in which n jobs are to be ...

Personal solutions to problem sets and labs from Harvard University's CS50: Introduction to Computer Science. Covers a wide range of programming languages including C, Python, JavaScript, and SQL.

Herein, through modeling and experiments, we demonstrate the general design strategy for single-component platinum micromotors that, in the presence of hydrogen peroxide, can execute a variety of ...

Because the population of interest is open and continually shifts over time, random samples from that population cannot be obtained in analytical studies, and traditional statistical methods are ...

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Linear programming and extensions by Dantzig, George B. (George Bernard), 1914-2005 Publication date 1963 Topics Linear programming, Mathematical models, Programmation linéaire, Modèles mathématiques, ...