Results 21 to 30 of about 22,543 (264)
Concentration inequalities for cross-validation in scattered data approximation [PDF]
Journal of Approximation Theory, 2022 Choosing models from a hypothesis space is a frequent task in approximation theory and inverse problems. Cross-validation is a classical tool in the learner's repertoire to compare the goodness of fit for different reconstruction models. Much work has been dedicated to computing this quantity in a fast manner but tackling its theoretical properties ...
Felix Bartel, Ralf Hielscher
openaire +3 more sources
Valid inequalities for mixed integer linear programs [PDF]
Mathematical Programming, 2007 This tutorial presents a theory of valid inequalities for mixed integer linear sets. It introduces the necessary tools from polyhedral theory and gives a geometric understanding of several classical families of valid inequalities such as lift-and-project cuts, Gomory mixed integer cuts, mixed integer rounding cuts, split cuts and intersection cuts, and
openaire +2 more sources
A condition for the validity of Fisher's inequality
Journal of the Japan Statistical Society, Japanese Issue, 1984 Summary: This gives new insights concerning conditions for the validity of Fisher's inequality. The theorem derived here includes some useful and combinatorially interesting results.
Kageyama, Sanpei, Tsuji, Takumi
openaire +2 more sources
Valid Inequalities and Convex Hulls for Multilinear Functions
Electronic Notes in Discrete Mathematics, 2010 We study the convex hull of the bounded, nonconvex set of a product of n variables for any n ≥ 2. We seek to derive strong valid linear inequalities for this set, which we call M_n; this is motivated by the fact that many exact solvers for nonconvex problems use polyhedral relaxations so as to compute a lower bound via linear programming solvers.
Belotti, Pietro +2 more
openaire +2 more sources
On the Justification and Validity of the Kennard Inequality [PDF]
American Journal of Modern Physics, 2020 In 1927, Earle Hesse Kennard derived an inequality describing Heisenberg’s uncertainty principle. Since then, we have traditionally been using the standard deviation as the measure of uncertainty in quantum mechanics. But Jan Hilgevoord asserts that the standard deviation is neither a natural nor a generally adequate measure of quantum uncertainty ...
openaire +1 more source
Mixed integer programming model with non-circular and guided constraints for architectural layout design optimization [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2008 Various techniques have been used to solve a challenging architecturallayout design problem for more than a decade, such as an expert system, an evolutionary algorithm, a simulated annealing and a mathematical programming method.
Kamol Keatruangkamala +1 more
doaj
Valid inequalities for mixed 0–1 programs
Discrete Applied Mathematics, 1986 A class of valid linear inequalities and facets for the convex hull is derived for a typical region appearing in mixed 0-1 programs. To use these inequalities computationally, it is shown how one can find a most violated inequality by solving a series of parametric equality knapsack problems with multiple choice constraints.
Tony J. Van Roy, Laurence A. Wolsey
openaire +1 more source
IEEE Open Journal of Intelligent Transportation SystemsEfficient logistics management is critical in the modern global supply chain, and this study introduces an advanced hyperheuristic approach to the Multi-Echelon Hub and Routing Optimization (MEHRO) problem.
Kassem Danach +3 more
doaj +1 more source
Scatter Search Algorithm for a Waste Collection Problem in an Argentine Case Study
Urban ScienceIncreasing urbanization and rising consumption rates are putting pressure on urban systems to efficiently manage Municipal Solid Waste (MSW). Waste collection, in particular, is one of the most challenging aspects of MSW management. Therefore, developing
Diego Rossit +3 more
doaj +1 more source
EURO Journal on Computational Optimization, 2015 We present O(n2)an integer linear formulation that uses the so-called “distance variables” to solve the quadratic assignment problem (QAP). The formulation performs particularly well for problems with Manhattan distance matrices.
Serigne Gueye, Philippe Michelon
doaj +1 more source