Algorithm Engineering [recurso electrónico] : Selected Results and Surveys / edited by Lasse Kliemann, Peter Sanders.
Tipo de material: TextoSeries Lecture Notes in Computer Science ; 9220Editor: Cham : Springer International Publishing : Imprint: Springer, 2016Descripción: X, 419 p. 68 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319494876Tema(s): Computer science | Computer communication systems | Computers | Algorithms | Computer science -- Mathematics | Artificial intelligence | Computer Science | Algorithm Analysis and Problem Complexity | Information Systems Applications (incl. Internet) | Artificial Intelligence (incl. Robotics) | Computer Communication Networks | Computation by Abstract Devices | Discrete Mathematics in Computer ScienceFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 005.1 Clasificación LoC:QA76.9.A43Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
---|---|---|---|---|---|---|---|
Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Engineering a Lightweight and Efficient Local Search SAT Solver -- Route Planning in Transportation Networks -- Theoretical Analysis of the k-Means Algorithm - A Survey -- Recent Advances in Graph Partitioning -- How to Generate Randomized Roundings with Dependencies and How to Derandomize Them -- External-Memory State Space Search -- Algorithm Engineering Aspects of Real-Time Rendering Algorithms -- Algorithm Engineering in Robust Optimization -- Clustering Evolving Networks -- Integrating Sequencing and Scheduling: A Generic Approach with Two Exemplary Industrial Applications -- Engineering a Bipartite Matching Algorithm in the Semi-Streaming Model -- Engineering Art Galleries.
Algorithm Engineering is a methodology for algorithmic research that combines theory with implementation and experimentation in order to obtain better algorithms with high practical impact. Traditionally, the study of algorithms was dominated by mathematical (worst-case) analysis. In Algorithm Engineering, algorithms are also implemented and experiments conducted in a systematic way, sometimes resembling the experimentation processes known from fields such as biology, chemistry, or physics. This helps in counteracting an otherwise growing gap between theory and practice.