1.

Record Nr.

UNINA9910482969203321

Titolo

Algorithm Engineering : Selected Results and Surveys / / edited by Lasse Kliemann, Peter Sanders

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016

ISBN

3-319-49487-2

Edizione

[1st ed. 2016.]

Descrizione fisica

1 online resource (X, 419 p. 68 illus.)

Collana

Theoretical Computer Science and General Issues, , 2512-2029 ; ; 9220

Disciplina

518.1

Soggetti

Algorithms

Application software

Artificial intelligence

Computer networks

Computer science

Computer science—Mathematics

Discrete mathematics

Computer and Information Systems Applications

Artificial Intelligence

Computer Communication Networks

Theory of Computation

Discrete Mathematics in Computer Science

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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.

Sommario/riassunto

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.