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Transport humanitaire et la logistique de crise : comparaison de deux méthodes de calcul de tournées de véhicules
Transport humanitaire et la logistique de crise : comparaison de deux méthodes de calcul de tournées de véhicules
Cet article étudie deux méthodes utilisées dans le cadre du transport humanitaire en cas de crise (désastre, épidémie...). Le Covering Tour Problem se focalise sur l'équité de distribution des vivres, alors que le Capacitated Vehicle Routing Problem se concentre sur l'urgence de la distribution. Nous proposons une nouvelle approche mélangeant ces deux approches pour former une solution à la fois équitable et rapide. Ce article a été rédigé dans le cadre du TER 2014-2015.
Dimitry Berardi, Abdelwahab Heba, Boris Terooatea, Maël Valais
Matrices
Matrices
En este documento se explican qué son las matrices, los distintos tipos que hay, algunas operaciones que se hacen con ellas con ellas (y sus respectivas propiedades), además de enseñar cómo realizar estas operaciones en programación. Incluye el concepto de relación binaria, y se explica cómo se pueden representar éstas (si son homogéneas) mediante grafos. y los distintos tipos de éstos últimos. Se recalca la "matriz jacobiana", explicando su función escalar y vectorial
Victoria
Resolução 3.9-b
Resolução 3.9-b
Resolução
Guilherme Baumgratz Figueiroa
Ejercicios en LaTeX
Ejercicios en LaTeX
Mathematic homework exercise in LaTeX
Alvaro Polo Ulloque
Gram-Schmidt Orthogonalization and QR decomposition
Gram-Schmidt Orthogonalization and QR decomposition
A very quick and easy to understand introduction to Gram-Schmidt Orthogonalization (Orthonormalization) and how to obtain QR decomposition of a matrix using it.
Mohd. Umar Rehman
Methods to prevent overfitting and solve ill-posed problems in statistics: Ridge Regression and LASSO
Methods to prevent overfitting and solve ill-posed problems in statistics: Ridge Regression and LASSO
Linear regression is one of the most widely used statistical methods available today. It is used by data analysts and students in almost every discipline. However, for the standard ordinary least squares method, there are several strong assumptions made about data that is often not true in real world data sets. This can cause numerous problems in the least squares model. One of the most common issues is a model overfitting the data. Ridge Regression and LASSO are two methods used to create a better and more accurate model. I will discuss how overfitting arises in least squares models and the reasoning for using Ridge Regression and LASSO include analysis of real world example data and compare these methods with OLS and each other to further infer the benefits and drawbacks of each method.
Chris Van Dusen
Avaliação de  Fundamentos Matemáticos para Computação 1
Avaliação de Fundamentos Matemáticos para Computação 1
Fundamentos Matemáticos para Computação 1 - 2015.2 - IMD Avaliação 2: Teoria dos Números, Congruência Autor: Patrick Terrematte
Patrick Cesar Alves Terrematte

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