User:Eigen Axon/Books/Optimization




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Optimization

Emphasizing Evolutionary Algorithm

Introduction: Mathematical optimization; Feasible region; Global optimum; Local optimum; Maxima and minima; Slack variable; Continuous optimization; Discrete optimization; Active set method; Candidate solution; Constraint (mathematics); Constrained optimization; Binary constraint; Corner solution
Linear Programming: Linear programming; Basic solution (linear programming); Hilbert basis (linear programming); Linear inequality; Vertex enumeration problem; Simplex algorithm; Bland's rule; Klee–Minty cube; Criss-cross algorithm; Big M method; Interior point method; Ellipsoid method; Karmarkar's algorithm; Mehrotra predictor–corrector method; Column generation; K-approximation of k-hitting set; Linear complementarity problem; Benders' decomposition; Dantzig–Wolfe decomposition; Theory of two-level planning; Variable splitting; Fourier–Motzkin elimination; LP-type problem
Convex Optimization: Convex optimization; Quadratic programming; Linear least squares (mathematics); Total least squares; Frank–Wolfe algorithm; Sequential minimal optimization; Bilinear program; Basis pursuit; Basis pursuit denoising; In-crowd algorithm; Linear matrix inequality; Conic optimization; Semidefinite programming; Second-order cone programming; Sum-of-squares optimization; Bregman method; Proximal gradient method; Subgradient method; Biconvex optimization
Nonlinear Programming: Nonlinear programming; Geometric programming; Signomial; Posynomial; Quadratically constrained quadratic program; Linear-fractional programming; Fractional programming; Nonlinear complementarity problem; Least squares; Non-linear least squares; Gauss–Newton algorithm; Berndt–Hall–Hall–Hausman algorithm; Generalized Gauss–Newton method; Levenberg–Marquardt algorithm; Iteratively reweighted least squares; Partial least squares regression; Non-linear iterative partial least squares; Golden section search; Successive parabolic interpolation; Mathematical programming with equilibrium constraints; Descent direction; Guess value; Line search; Backtracking line search; Wolfe conditions; Gradient method; Gradient descent; Stochastic gradient descent; Derivation of the conjugate gradient method; Conjugate gradient method; Biconjugate gradient method; Nonlinear conjugate gradient method; Landweber iteration; Successive linear programming; Sequential quadratic programming; Newton's method in optimization; Coordinate descent; Adaptive coordinate descent; Random coordinate descent; Nelder–Mead method; Pattern search (optimization); Powell's method; Rosenbrock methods; Augmented Lagrangian method; Ternary search; Tabu search; Guided Local Search; LIONsolver; MM algorithm; Least absolute deviations; Expectation–maximization algorithm; Ordered subset expectation maximization; Adaptive projected subgradient method; Nearest neighbor search; Space mapping
Infinite-Dimension Optimization: Optimal control; Pontryagin's minimum principle; Costate equations; Hamiltonian (control theory); Linear-quadratic regulator; Linear-quadratic-Gaussian control; Optimal projection equations; Algebraic Riccati equation; Bang–bang control; Covector mapping principle; Differential dynamic programming; DNSS point; Legendre–Clebsch condition; Pseudospectral optimal control; Bellman pseudospectral method; Chebyshev pseudospectral method; Flat pseudospectral method; Gauss pseudospectral method; Legendre pseudospectral method; Pseudospectral knotting method; Ross–Fahroo pseudospectral method; Ross–Fahroo lemma; Ross' π lemma; Sethi model; Infinite-dimensional optimization; Semi-infinite programming; Shape optimization; Topology optimization; Topological derivative; Generalized semi-infinite programming
Stochastic: Stochastic optimization; Stochastic programming; Stochastic approximation; Markov decision process; Partially observable Markov decision process; Probabilistic-based design optimization; Robust optimization; Wald's maximin model; Scenario optimization; Random optimization; Random search; Simulated annealing; Adaptive simulated annealing; Great Deluge algorithm; Mean field annealing; Bayesian optimization; Luus–Jaakola; Stochastic tunneling; Harmony search; Monte Carlo method; Direct simulation Monte Carlo; Quasi-Monte Carlo method; Markov chain Monte Carlo; Metropolis–Hastings algorithm; Pseudo-random number sampling; Variance reduction
Evolutionary Algorithms: Artificial intelligence; Metaheuristic; Evolutionary algorithm; Fitness function; Evolutionary computation; Evolutionary programming; Gene expression programming; Differential evolution; Genetic algorithm; Genetic programming; Genetic algorithms in economics; MCACEA; Simultaneous perturbation stochastic approximation; Evolution strategy; Neuroevolution; Learning classifier system; Swarm intelligence; Ant colony optimization algorithms; Artificial bee colony algorithm; Particle swarm optimization; Cuckoo search; Bees algorithm; Artificial immune system; Bat algorithm; Glowworm swarm optimization; Self-propelled particles; Stochastic diffusion search; Multi-swarm optimization; Firefly algorithm; Memetic algorithm