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Lecture

Rhino to AutoCAD Transition

In course

AR-219: Advanced CAO and Integrated Modeling DIM

1ère année: bases nécessaires à la représentation informatique 2D (3D). Passage d'un à plusieurs logiciels: compétence de choisir les outils adéquats en 2D et en 3D. Mise en relation des outils de CAO et DIM: regard critique et aptitude à choisir les métho ...

Description

This lecture covers the transition from Rhino to AutoCAD, focusing on tools for subd, grid magnetism, ortho, flatness, snaps, and intelligent tracking. It also explores manipulator features, record history, and memory usage optimization.

Instructor

Jérôme Zufferey

Diplôme d'Architecture à l'EPFL en 1995. Certificat Eur'IC (European Informatics Conference) en 1997. Brevet Fédéral de Formateur d'Adultes en 2006. Bureau d'informaticien, formateur et architecte indépendant depuis 1997. Travaux d'installation, support, conseil et formation auprès d'entreprises. Travaux d'infographie auprès de bureaux d'architectures et d'entreprises (relevés d'usines, plans techniques, modélisations, photomontages, plaquettes de concours, ...) Domaines d'intérêts spécifiques: L'informatique dans son histoire et son évolution (premiers pas avec un Apple ][ en 1981), l'infographie dans sa multiplicité d'outils et sa problématique vis à vis de la représentation architecturale, la pédagogie dans sa constante remise en question qu'elle implique, la vie sociale et politique (Conseiller communal à Bex, 2002-2008). Dès 1999, assistant chez Monsieur le Professeur Georges Abou Jaoudé (LIV, cours d'informatique pour les étudiants en architecture de deuxième année avec Monsieur Michel Herezen, chargé de cours jusqu'en 2010). Aujourd'hui Informaticien ENAC-IT3 et Chargé de cours ENAC-SAR.

Official source

Related concepts (32)

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Combinatorial optimization

Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set. Typical combinatorial optimization problems are the travelling salesman problem ("TSP"), the minimum spanning tree problem ("MST"), and the knapsack problem. In many such problems, such as the ones previously mentioned, exhaustive search is not tractable, and so specialized algorithms that quickly rule out large parts of the search space or approximation algorithms must be resorted to instead.

Program optimization

In computer science, program optimization, code optimization, or software optimization, is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. In general, a computer program may be optimized so that it executes more rapidly, or to make it capable of operating with less memory storage or other resources, or draw less power. Although the word "optimization" shares the same root as "optimal", it is rare for the process of optimization to produce a truly optimal system.

Convex optimization

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets). Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard.

Optimizing compiler

In computing, an optimizing compiler is a compiler that tries to minimize or maximize some attributes of an executable computer program. Common requirements are to minimize a program's execution time, memory footprint, storage size, and power consumption (the last three being popular for portable computers). Compiler optimization is generally implemented using a sequence of optimizing transformations, algorithms which take a program and transform it to produce a semantically equivalent output program that uses fewer resources or executes faster.

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