From the Publisher:
A valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography, this book provides easy and rapid access of information and includes more than 200 algorithms and protocols; more than 200 tables and figures; more than 1,000 numbered definitions, facts, examples, notes, and remarks; and over 1,250 significant references, including brief comments on each paper.

Handbook of Applied Cryptography

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Preface, Notations 1.Introduction to Time-Delay Systems I.Frequency-Domain Approach 2.Systems with Commensurate Delays 3.Systems withIncommensurate Delays 4.Robust Stability Analysis II.Time Domain Approach 5.Systems with Single Delay 6.Robust Stability Analysis 7.Systems with Multiple and Distributed Delays III.Input-Output Approach 8.Input-output stability A.Matrix Facts B.LMI and Quadratic Integral Inequalities Bibliography Index

/pdf/stability-of-time-delay-systems-5d2qjgdknf.pdf

Stability of Time-Delay Systems

In semidefinite programming, one minimizes a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmooth, but convex, so semidefinite programs are convex optimization problems. Semidefinite programming unifies several standard problems (e.g., linear and quadratic programming) and finds many applications in engineering and combinatorial optimization. Although semidefinite programs are much more general than linear programs, they are not much harder to solve. Most interior-point methods for linear programming have been generalized to semidefinite programs. As in linear programming, these methods have polynomial worst-case complexity and perform very well in practice. This paper gives a survey of the theory and applications of semidefinite programs and an introduction to primaldual interior-point methods for their solution.

Semidefinite programming

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

/pdf/understanding-machine-learning-from-theory-to-algorithms-5aeszqx33g.pdf

Understanding Machine Learning: From Theory To Algorithms

1 Introduction.- 2 Definitions and First Properties of Computation.- 3 Computation over a Ring.- 4 Decision Problems and Complexity over a Ring.- 5 The Class NP and NP-Complete Problems.- 6 Integer Machines.- 7 Algebraic Settings for the Problem "P ? NP?".- 8 Newton's Method.- 9 Fundamental Theorem of Algebra: Complexity Aspects.- 10 Bezout's Theorem.- 11 Condition Numbers and the Loss of Precision of Linear Equations.- 12 The Condition Number for Nonlinear Problems.- 13 The Condition Number in ?(H(d).- 14 Complexity and the Condition Number.- 15 Linear Programming.- 16 Deterministic Lower Bounds.- 17 Probabilistic Machines.- 18 Parallel Computations.- 19 Some Separations of Complexity Classes.- 20 Weak Machines.- 21 Additive Machines.- 22 Nonuniform Complexity Classes.- 23 Descriptive Complexity.- References.

Complexity and Real Computation

/pdf/on-a-theory-of-computation-and-complexity-over-the-real-2s93av83k1.pdf

On a theory of computation and complexity over the real numbers: $NP$- completeness, recursive functions and universal machines

Intelligence tests occasionally require the extrapolation of an effective sequence (e.g. 1661, 2552, 3663, …) that is produced by some easily discernible algorithm. In this paper, we investigate the theoretical capabilities and limitations of a computer to infer such sequences. We design Turing machines that in principle are extremely powerful for this purpose and place upper bounds on the capabilities of machines that would do better.

Toward a mathematical theory of inductive inference

What do we want from a pseudo-random sequence generator? Ideally, we would like a pseudo-random sequence generator to quickly produce, from short seeds, long sequences (of bits) that appear in every way to be generated by successive flips of a fair coin.

Comparison of Two Pseudo-Random Number Generators

In this paper, we describe a pilot summer workshop (CS4HS) held at Carnegie Mellon University in July 2006 for high school CS teachers to provide compelling material that the teachers can use in their classes to emphasize computational thinking and the many possibilities of computer science. Diversity and broadening participation was explicitly addressed throughout the workshop. We focused on broadening the image of what CS is -- and who computer scientists are -- since the reasons for under-representation in the field are very much the same as the reasons for the huge decline in interest. We describe the design of the workshop along with results from initial surveys and evaluations. Short-term evaluations show that this workshop was successful in changing the perception of CS for these teachers and giving them the impetus to include broader topics in their programming courses for the upcoming school year. Future surveys will track the long-term effect of this workshop.

Lenore Blum

Papers

Complexity and Real Computation

On a theory of computation and complexity over the real numbers: $NP$- completeness, recursive functions and universal machines

Toward a mathematical theory of inductive inference

Comparison of Two Pseudo-Random Number Generators

CS4HS: an outreach program for high school CS teachers