User:Eigen Axon/Books/Optimization
 | The Wikimedia Foundation's book rendering service has been withdrawn. Please upload your Wikipedia book to one of the external rendering services. |
 | You can still create and edit a book design using the Book Creator and upload it to an external rendering service:
|
| This user book is a user-generated collection of Wikipedia articles that can be easily saved, rendered electronically, and ordered as a printed book. If you are the creator of this book and need help, see Help:Books (general tips) and WikiProject Wikipedia-Books (questions and assistance). Edit this book: Book Creator · Wikitext Order a printed copy from: PediaPress [ About ] [ Advanced ] [ FAQ ] [ Feedback ] [ Help ] [ WikiProject ] [ Recent Changes ] | ||||||||
Optimization[edit]
Emphasizing Evolutionary Algorithm[edit]
- 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