# Literature

**Goodreads**to keep track of books and find book reviews.

## Popular Science

*Sapiens*, *Homo Deus*, and *21 lessons for the 21st-century*, by Yuval Noah Harari, is three-part book series discussing the past, present, and the future of humankind and society.

*The Big Picture*, by Sean Caroll, explores the universe, fundamental concepts physics and the origins of life.

*The Master Algorithm*, by Pedro Domingos, discusses the five tribes of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem, and analogical modeling, and the challenge of unifying them into one master algorithm.

## Mathematics

*Logic and Proof*, by Jeremy Avigad, Robert Y. Lewis, and Floris van Doorn, covers topics on logic and theorem proving including propositional logic, set theory, relations, functions, and combinatorics. They wrote the book as a companion with the *Lean* theorem prover.

*Contemporary Abstract Algebra*, by Joseph Gallian, explains the fundamental algebraic structures; groups, rings, and fields.

*Introduction to Linear Algebra*, by Gilbert Strang, covers properties of vectors and matrices and operations for solving linear equations.

*Calculus*, by Robert A. Adams and Christopher Essex, covers the fundamental topics in calculus including limits, differentiation, integration, differential equations, and series.

*Nonlinear programming: Theory and Algorithms*, by Mokhtar S. Bazaraa, Hanif D. Sherali, and C. M. Shetty, covers convex analysis, optimality conditions, duality, and algorithms for solving unconstrained and constrained nonlinear programming problems with their convergence.

*Integer programming*, by Laurence A. Wolsey, covers ways to solve optimization problems with discrete or integer variables.

*Mosek Modeling Cookbook* is a useful reference for formulating optimization models.

*The Matrix Cookbook*, by Kaare Brandt Petersen and Michael Syskind Pedersen, is a reference for matrix operations.

Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications

*NIST Handbook of Mathematical Functions* is a reference for mathematical functions.

## Computer Science

*Introduction to Algorithms*, by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, covers the fundamentals concepts of algorithms and data structures.

*Computational Complexity*, by Christos Papadimitriou, explains fundamental topics in computational complexity theory such as Turing machines and complexity classes.

*Modern Computer Algebra*, by Joachim von zur Gathen and Jürgen Gerhard, covers the design of efficient algorithms for operations on polynomials and integers, including multiplication and evaluation, and interpolation.

*Modern Computer Arithmetic*, by Richard Brent and Paul Zimmermann, covers the design of efficient algorithms for integer, modular, and floating-point arithmetic.

*Deep Learning*, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, covers basics of machine learning, deep learning, and the concepts in applied mathematics on which they stand.

*Software Engineering*, by Ian Sommerville, covers different software engineering practices, including software development methods, modeling, design, testing, and management.

*Operating Systems*, by William Stallings, covers the fundamentals principles of operating systems.

*Computer Security*, by William Stallings, covers the fundamental principles of computer security technology.

*Bitcoin and Cryptocurrency Technologies*, by Arvind Narayanan, Joseph Bonneau, Edward W. Felten, Andrew Miller, Steven Goldfeder, and Jeremy Clark

The Wolfram Physics Project: A Project to Find the Fundamental Theory Physics

## Finance

*Corporate Finance*, by Jonathan Berk and Peter DeMarzo, covers core concepts for solving quantitative business problems.