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This book is a spectacular introduction to the modern mathematical discipline known as the Theory of Games. Harold Kuhn first presented these lectures at Princeton University in 1952. They succinctly convey the essence of the theory, in part through the prism of the most exciting developments at its frontiers half a century ago. Kuhn devotes considerable space to topics that, while not strictly the subject matter of game theory, are firmly bound to it. These are taken mainly from the geometry of convex sets and the theory of probability distributions. The book opens by addressing "matrix games," a name first introduced in these lectures as an abbreviation for two-person, zero-sum games in normal form with a finite number of pure strategies. It continues with a treatment of games in extensive form, using a model introduced by the author in 1950 that quickly supplanted von Neumann and Morgenstern's cumbersome approach. A final section deals with games that have an infinite number of pure strategies for the two players. Throughout, the theory is generously illustrated with examples, and exercises test the reader's understanding. A historical note caps off each chapter. For readers familiar with the calculus and with elementary matrix theory or vector analysis, this book offers an indispensable store of vital insights on a subject whose importance has only grown with the years.

Operational research. Game theory --- Game theory --- 519.83 --- Theory of games --- 519.83 Theory of games --- Game theory. --- Games, Theory of --- Mathematical models --- Mathematics --- Abstract algebra. --- Addition. --- Algorithm. --- Almost surely. --- Analytic geometry. --- Axiom. --- Basic solution (linear programming). --- Big O notation. --- Bijection. --- Binary relation. --- Boundary (topology). --- Bounded set (topological vector space). --- Branch point. --- Calculation. --- Cardinality of the continuum. --- Cardinality. --- Cartesian coordinate system. --- Characteristic function (probability theory). --- Combination. --- Computation. --- Connectivity (graph theory). --- Constructive proof. --- Convex combination. --- Convex function. --- Convex hull. --- Convex set. --- Coordinate system. --- David Gale. --- Diagram (category theory). --- Differential equation. --- Dimension (vector space). --- Dimensional analysis. --- Disjoint sets. --- Distribution function. --- Embedding. --- Empty set. --- Enumeration. --- Equation. --- Equilibrium point. --- Equivalence relation. --- Estimation. --- Euclidean space. --- Existential quantification. --- Expected loss. --- Extreme point. --- Formal scheme. --- Fundamental theorem. --- Galois theory. --- Geometry. --- Hyperplane. --- Inequality (mathematics). --- Infimum and supremum. --- Integer. --- Iterative method. --- Line segment. --- Linear equation. --- Linear inequality. --- Matching Pennies. --- Mathematical induction. --- Mathematical optimization. --- Mathematical theory. --- Mathematician. --- Mathematics. --- Matrix (mathematics). --- Measure (mathematics). --- Min-max theorem. --- Minimum distance. --- Mutual exclusivity. --- Prediction. --- Probability distribution. --- Probability interpretations. --- Probability measure. --- Probability theory. --- Probability. --- Proof by contradiction. --- Quantity. --- Rank (linear algebra). --- Rational number. --- Real number. --- Requirement. --- Scientific notation. --- Sign (mathematics). --- Solution set. --- Special case. --- Statistics. --- Strategist. --- Strategy (game theory). --- Subset. --- Theorem. --- Theory of Games and Economic Behavior. --- Theory. --- Three-dimensional space (mathematics). --- Total order. --- Two-dimensional space. --- Union (set theory). --- Unit interval. --- Unit square. --- Vector Analysis. --- Vector calculus. --- Vector space.

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This book represents the first synthesis of the considerable body of new research into positive definite matrices. These matrices play the same role in noncommutative analysis as positive real numbers do in classical analysis. They have theoretical and computational uses across a broad spectrum of disciplines, including calculus, electrical engineering, statistics, physics, numerical analysis, quantum information theory, and geometry. Through detailed explanations and an authoritative and inspiring writing style, Rajendra Bhatia carefully develops general techniques that have wide applications in the study of such matrices. Bhatia introduces several key topics in functional analysis, operator theory, harmonic analysis, and differential geometry--all built around the central theme of positive definite matrices. He discusses positive and completely positive linear maps, and presents major theorems with simple and direct proofs. He examines matrix means and their applications, and shows how to use positive definite functions to derive operator inequalities that he and others proved in recent years. He guides the reader through the differential geometry of the manifold of positive definite matrices, and explains recent work on the geometric mean of several matrices. Positive Definite Matrices is an informative and useful reference book for mathematicians and other researchers and practitioners. The numerous exercises and notes at the end of each chapter also make it the ideal textbook for graduate-level courses.

Matrices. --- Algebra, Matrix --- Cracovians (Mathematics) --- Matrix algebra --- Matrixes (Algebra) --- Algebra, Abstract --- Algebra, Universal --- Matrices --- 512.64 --- 512.64 Linear and multilinear algebra. Matrix theory --- Linear and multilinear algebra. Matrix theory --- Addition. --- Analytic continuation. --- Arithmetic mean. --- Banach space. --- Binomial theorem. --- Block matrix. --- Bochner's theorem. --- Calculation. --- Cauchy matrix. --- Cauchy–Schwarz inequality. --- Characteristic polynomial. --- Coefficient. --- Commutative property. --- Compact space. --- Completely positive map. --- Complex number. --- Computation. --- Continuous function. --- Convex combination. --- Convex function. --- Convex set. --- Corollary. --- Density matrix. --- Diagonal matrix. --- Differential geometry. --- Eigenvalues and eigenvectors. --- Equation. --- Equivalence relation. --- Existential quantification. --- Extreme point. --- Fourier transform. --- Functional analysis. --- Fundamental theorem. --- G. H. Hardy. --- Gamma function. --- Geometric mean. --- Geometry. --- Hadamard product (matrices). --- Hahn–Banach theorem. --- Harmonic analysis. --- Hermitian matrix. --- Hilbert space. --- Hyperbolic function. --- Infimum and supremum. --- Infinite divisibility (probability). --- Invertible matrix. --- Lecture. --- Linear algebra. --- Linear map. --- Logarithm. --- Logarithmic mean. --- Mathematics. --- Matrix (mathematics). --- Matrix analysis. --- Matrix unit. --- Metric space. --- Monotonic function. --- Natural number. --- Open set. --- Operator algebra. --- Operator system. --- Orthonormal basis. --- Partial trace. --- Positive definiteness. --- Positive element. --- Positive map. --- Positive semidefinite. --- Positive-definite function. --- Positive-definite matrix. --- Probability measure. --- Probability. --- Projection (linear algebra). --- Quantity. --- Quantum computing. --- Quantum information. --- Quantum statistical mechanics. --- Real number. --- Riccati equation. --- Riemannian geometry. --- Riemannian manifold. --- Riesz representation theorem. --- Right half-plane. --- Schur complement. --- Schur's theorem. --- Scientific notation. --- Self-adjoint operator. --- Sign (mathematics). --- Special case. --- Spectral theorem. --- Square root. --- Standard basis. --- Summation. --- Tensor product. --- Theorem. --- Toeplitz matrix. --- Unit vector. --- Unitary matrix. --- Unitary operator. --- Upper half-plane. --- Variable (mathematics).