The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious Organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. In Parallel to the printed book, each new volume is published electronically in LNCS Online.

This book constitutes the proceedings of the 12th International Scandinavian Workshop on Algorithm Theory, held in Bergen, Norway in June 2010.

This book constitutes the refereed proceedings of the 14th International Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2014, held in Copenhagen, Denmark, in July 2014. The 33 papers were carefully reviewed and selected from a total of 134 submissions. The papers present original research and cover a wide range of topics in the field of design and analysis of algorithms and data structures including but not limited to approximation algorithms, parameterized algorithms, computational biology, computational geometry and topology, distributed algorithms, external-memory algorithms, exponential algorithms, graph algorithms, online algorithms, optimization algorithms, randomized algorithms, streaming algorithms, string algorithms, sublinear algorithms and algorithmic game theory.

This volume constitutes the refereed proceedings of the 35th International Symposium on Mathematical Foundations of Computer Science, MFCS 2010, held in Brno, Czech Republic, in August 2010. The 56 revised full papers presented together with 5 invited talks were carefully reviewed and selected from 149 submissions. Topics covered include algorithmic game theory, algorithmic learning theory, algorithms and data structures, automata, grammars and formal languages, bioinformatics, complexity, computational geometry, computer-assisted reasoning, concurrency theory, cryptography and security, databases and knowledge-based systems, formal specifications and program development, foundations of computing, logic in computer science, mobile computing, models of computation, networks, parallel and distributed computing, quantum computing, semantics and verification of programs, and theoretical issues in artificial intelligence.

This book constitutes the refereed proceedings of the 7th International Conference on Theory and Applications of Models of Computation, TAMC 2010, held in Prague, Czech Republic, in June 2010. The 35 revised full papers presented together with 5 contributions of special sessions as well as 2 plenary talks were carefully reviewed and selected from 76 submissions. The papers address the three main themes of the conference which were computability, complexity, and algorithms and present current research in these fields with aspects to theoretical computer science, algorithmic mathematics, and applications to the physical sciences.

For a long time computer scientists have distinguished between fast and slow algo rithms. Fast (or good) algorithms are the algorithms that run in polynomial time, which means that the number of steps required for the algorithm to solve a problem is bounded by some polynomial in the length of the input. All other algorithms are slow (or bad). The running time of slow algorithms is usually exponential. This book is about bad algorithms. There are several reasons why we are interested in exponential time algorithms. Most of us believe that there are many natural problems which cannot be solved by polynomial time algorithms. The most famous and oldest family of hard problems is the family of NP complete problems. Most likely there are no polynomial time al gorithms solving these hard problems and in the worst case scenario the exponential running time is unavoidable. Every combinatorial problem is solvable in ?nite time by enumerating all possi ble solutions, i. e. by brute force search. But is brute force search always unavoid able? De?nitely not. Already in the nineteen sixties and seventies it was known that some NP complete problems can be solved signi?cantly faster than by brute force search. Three classic examples are the following algorithms for the TRAVELLING SALESMAN problem, MAXIMUM INDEPENDENT SET, and COLORING.