The science of complexity aims to answer the question of what rules nature chooses when
assembling the basic constituents of matter and energy into structures and dynamical …

Randomized algorithms, once viewed as a tool in computational number theory, have by
now found widespread application. Growth has been fueled by the two major benefits of …

Modern science often requires the execution of large-scale, multi-stage simulation and data
analysis pipelines to enable the study of complex systems. The amount of computation and …

Phylogenies provide a useful way to understand the evolutionary history of genetic samples,
and data sets with more than a thousand taxa are becoming increasingly common, notably …

Combining concepts from topology and algorithms, this book delivers what its title promises:
an introduction to the field of computational topology. Starting with motivating problems in …

Over the last decade, there has been considerable interest in designing algorithms for
processing massive graphs in the data stream model. The original motivation was two-fold …

Many areas of science depend on exploratory data analysis and visualization. The need to
analyze large amounts of multivariate data raises the fundamental problem of dimensionality …

Scientists working with large volumes of high-dimensional data, such as global climate
patterns, stellar spectra, or human gene distributions, regularly confront the problem of …

A comprehensive update of the leading algorithms text, with new material on matchings in
bipartite graphs, online algorithms, machine learning, and other topics. Some books on …

Automata, Computability, and Complexity 1/Complexity theory 2/Computability theory
2/Automata theory 3/Mathematical Notions and Terminology 3/Sets 3/Sequences and tuples …