About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 253. Chapters: Convex analysis, Generalizations of the derivative, Mathematical optimization, Variational analysts, P versus NP problem, Pareto efficiency, Operations research, Gradient, Convex set, Genetic algorithm, Least squares, Dynamic programming, Genetic programming, Semi-continuity, Quadratic programming, Random optimization, Calculus of variations, Lagrange multiplier, Convex hull, Optimal design, Metaheuristic, Differential of a function, Optimal control, Lie derivative, Non-linear least squares, NP-complete, Oriented matroid, No free lunch in search and optimization, Ordinal optimization, Legendre transformation, Multidisciplinary design optimization, Convex function, Distributed constraint optimization, Bellman equation, Hyper-heuristic, Nearest neighbor search, Semidefinite programming, Least absolute deviations, Robust optimization, Radon-Nikodym theorem, Starmad, Directional derivative, Gateaux derivative, Jacobian matrix and determinant, Klee-Minty cube, Wald's maximin model, Frechet derivative, Exterior derivative, Geometric median, Maxima and minima, Compressed sensing, Google matrix, Linear complementarity problem, Leonid Kantorovich, Trajectory optimization, Quasiconvex function, Differential evolution, Multi-objective optimization, Wing shape optimization, Convex optimization, R. Tyrrell Rockafellar, Jeep problem, Robert Phelps, Response surface methodology, Backward induction, Minkowski addition, Dead-end elimination, Karush-Kuhn-Tucker conditions, Pushforward, Convex conjugate, Convex cone, Energy minimization, Topology optimization, Subgradient method, Level set method, MPS, AMPL, Job shop scheduling, APMonitor, Nonlinear programming, Pontryagin's minimum principle, Odds algorithm, Dual problem, Meta-optimization, Differintegral, Linear-fractional programming, Goal programming, Variational Monte Carlo, Max...
