Maths for computer scientists

Wallis's book on discrete mathematics is a resource for an introductory course in a subject fundamental to both mathematics and computer science, a course that is expected not only to cover certain specific topics but also to introduce students to important modes of thought specific to each discipline .

Because of the increasing complexity and growth of real-world networks, their analysis by using classical graph-theoretic methods is oftentimes a difficult procedure.

Magic squares are among the more popular mathematical recreations.

A cornerstone of undergraduate mathematics, science, and engineering, this clear and rigorous presentation of the fundamentals of linear algebra is unique in its emphasis and integration of computational skills and mathematical abstractions.

Introduction to Probability with Statistical Applications targets non-mathematics students, undergraduates and graduates, who do not need an exhaustive treatment of the subject.

This monograph, derived from an advanced computer science course at Stanford University, builds on the fundamentals of combinatorial analysis and complex variable theory to present many of the major paradigms used in the precise analysis of algorithms, emphasizing the more difficult notions.

Scan statistics is currently one of the most active and important areas of research in applied probability and statistics, having applications to a wide variety of fields: archaeology, astronomy, bioinformatics, biosurveillance, molecular biology, genetics, computer science, electrical engineering, geography, material sciences, physics, reconnaissance, reliability and quality control, telecommunication, and epidemiology.

Networks have become nearly ubiquitous and increasingly complex, and their support of modern enterprise environments has become fundamental.

In his new undergraduate textbook, Harold M.

"e;An important topic, which is on the boundary between numerical analysis and computer science .

This book has two main objectives: *  to provide a concise introduction to nonlinear optimization methods, which can be used as a textbook at a graduate or upper undergraduate level; *  to collect and organize selected important topics on optimization algorithms, not easily found in textbooks, which can provide material for advanced courses or can serve as a reference text for self-study and research.

Combinatorial research has proceeded vigorously in Russia over the last few decades, based on both translated Western sources and original Russian material.

Algorithms for Computer Algebra is the first comprehensive textbook to be published on the topic of computational symbolic mathematics.

This book presents a short introduction to the main tools of optimization methodology including linear programming, steepest descent, conjugate gradients, and the Karush-Kuhn-Tucker-John conditions.

Can you trust results from modelling and simulation?

This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models of complex systems.

Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations.

Fundamental concepts of mathematical modeling Modeling is one of the most effective, commonly used tools in engineering and the applied sciences.

Emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results.

Finite Element Methods are used for numerous engineering applications where numerical solutions of partial differential equations are needed.

Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation.

Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods.

"e;Information Theory and Statistical Learning"e; presents theoretical and practical results about information theoretic methods used in the context of statistical learning.

This book gives you a step-by-step introduction to analysing time series using the open source software R.

Monte Carlo methods are among the most used and useful computational tools available today, providing efficient and practical algorithims to solve a wide range of scientific and engineering problems.

While we were busy putting together the present collection of articles celebrating the twentieth birthday of our journal, Discrete & Computational Geometry, and, in a way, of the ?

Programming Languages: An Active Learning Approach introduces students to three programming paradigms: object-oriented/imperative languages using C++ and Ruby, functional languages using Standard ML, and logic programming using Prolog.

This book will be a valuable reference for researchers in the eare of nonparametrics.

Optimization is a field important in its own right but is also integral to numerous applied sciences, including operations research, management science, economics, finance and all branches of mathematics-oriented engineering.

This second edition presents up-to-date material on the theory of weak convergance of convolution products of probability measures in semigroups, the theory of random walks on semigroups, and their applications to products of random matrices.

The Primality Testing Problem (PTP) has now proved to be solvable in deterministic polynomial-time (P) by the AKS (Agrawal-Kayal-Saxena) algorithm, whereas the Integer Factorization Problem (IFP) still remains unsolvable in (P).

Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical.

In this monograph, new combinatorial and computational approaches in the study of RNA structures are presented which enhance both mathematics and computational biology.

It's hard to believe that it's been over a decade since One Jump Ahead: Challenging Human Supremacy at Checkers was published.

Recent Advances in Numerical Methods features contributions from distinguished researchers focused on significant aspects of current numerical methods and computational mathematics.

The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics.

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.

The main intended audience for this book is undergraduate students in pure and applied sciences, especially those in engineering.

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty.

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory.

Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics.

The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.

Practical Optimization: Algorithms and Engineering Applications provides a hands-on treatment of the subject of optimization.

Gluecklich, die wissen, dass hinter allen Sprachen das Unsaegliche steht.

Introduction to Discrete Event Systems is a comprehensive introduction to the field of discrete event systems, offering a breadth of coverage that makes the material accessible to readers of varied backgrounds.

Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty.

Sequential Dynamical Systems (SDS) are a class of discrete dynamical systems which significantly generalize many aspects of systems such as cellular automata, and provide a framework for studying dynamical processes over graphs.

Essential Mathematica: With Applications to Mathematics and Physics, based on the lecture notes of a course taught at the University of Illinois at Chicago to advanced undergrad and graduate students, teaches how to use Mathematica to solve a wide variety problems in mathematics and physics.