Editing List of algorithms (section)

===Optimization algorithms===
{{main|Mathematical optimization}}[[Hybrid algorithm|Hybrid]] Algorithms
* [[Alpha–beta pruning]]: search to reduce number of nodes in minimax algorithm
* [[Branch and bound]]
* [[Bruss algorithm]]: see [[odds algorithm]]
* [[Chain matrix multiplication]]
* [[Combinatorial optimization]]: optimization problems where the set of feasible solutions is discrete
** [[Greedy randomized adaptive search procedure]] (GRASP): successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search
** [[Hungarian method]]: a combinatorial optimization algorithm which solves the [[assignment problem]] in polynomial time
* [[Constraint satisfaction]]{{anchor|Constraint satisfaction}}
** General algorithms for the constraint satisfaction
*** [[AC-3 algorithm]]
*** [[Difference map algorithm]]
*** [[Min conflicts algorithm]]
** [[Chaff algorithm]]: an algorithm for solving instances of the [[Boolean satisfiability problem]]
** [[Davis–Putnam algorithm]]: check the validity of a first-order logic formula
** [[DPLL algorithm|Davis–Putnam–Logemann–Loveland algorithm]] (DPLL): an algorithm for deciding the satisfiability of propositional logic formula in [[conjunctive normal form]], i.e. for solving the [[CNF-SAT]] problem
** [[Exact cover]] problem
*** [[Algorithm X]]: a [[nondeterministic algorithm]]
*** [[Dancing Links]]: an efficient implementation of Algorithm X
* [[Cross-entropy method]]: a general Monte Carlo approach to combinatorial and continuous multi-extremal optimization and [[importance sampling]]
* [[Differential evolution]]
* [[Dynamic Programming]]: problems exhibiting the properties of [[overlapping subproblem]]s and [[optimal substructure]]
* [[Ellipsoid method]]: is an algorithm for solving convex optimization problems
* [[Evolutionary computation]]: optimization inspired by biological mechanisms of evolution
** [[Evolution strategy]]
** [[Gene expression programming]]
** [[Genetic algorithms]]
*** [[Fitness proportionate selection]] – also known as roulette-wheel selection
*** [[Stochastic universal sampling]]
*** [[Truncation selection]]
*** [[Tournament selection]]
** [[Memetic algorithm]]
** [[Swarm intelligence]]
*** [[Ant colony optimization]]
*** [[Bees algorithm]]: a search algorithm which mimics the food foraging behavior of swarms of honey bees
*** [[Particle swarm optimization|Particle swarm]]
* [[Frank-Wolfe algorithm]]: an iterative first-order optimization algorithm for constrained convex optimization
* [[Golden-section search]]: an algorithm for finding the maximum of a real function
* [[Gradient descent]]
* [[Hyperparameter optimization#Grid search|Grid Search]]
* [[Harmony search]] (HS): a [[metaheuristic]] algorithm mimicking the improvisation process of musicians
* [[Interior point method]]
* {{anchor|Linear programming}}[[Linear programming]]
** [[Benson's algorithm]]: an algorithm for solving linear [[vector optimization]] problems
** [[Dantzig–Wolfe decomposition]]: an algorithm for solving linear programming problems with special structure
** [[Delayed column generation]]
** [[Integer linear programming]]: solve linear programming problems where some or all the unknowns are restricted to integer values
*** [[Branch and cut]]
*** [[Cutting-plane method]]
** [[Karmarkar's algorithm]]: The first reasonably efficient algorithm that solves the [[linear programming]] problem in [[polynomial time]].
** [[Simplex algorithm]]: an algorithm for solving [[linear programming]] problems
* [[Line search]]
* [[Local search (optimization)|Local search]]: a metaheuristic for solving computationally hard optimization problems
** [[Random-restart hill climbing]]
** [[Tabu search]]
* [[Minimax#Minimax algorithm with alternate moves|Minimax]] used in game programming
* [[Nearest neighbor search]] (NNS): find closest points in a [[metric space]]
** [[Best Bin First]]: find an approximate solution to the [[nearest neighbor search]] problem in very-high-dimensional spaces
* [[Newton's method in optimization]]
* [[Nonlinear optimization]]
** [[BFGS method]]: a [[nonlinear optimization]] algorithm
** [[Gauss–Newton algorithm]]: an algorithm for solving nonlinear [[least squares]] problems
** [[Levenberg–Marquardt algorithm]]: an algorithm for solving nonlinear [[least squares]] problems
** [[Nelder–Mead method]] (downhill simplex method): a [[nonlinear optimization]] algorithm
* [[Odds algorithm]] (Bruss algorithm): Finds the optimal strategy to predict a last specific event in a random sequence event
* [[Hyperparameter optimization#Random search|Random Search]]
* [[Simulated annealing]]
* [[Stochastic tunneling]]
* [[Subset sum problem|Subset sum]] algorithm
* [[A hybrid HS-LS conjugate]] [[gradient algorithm]] (see https://doi.org/10.1016/j.cam.2023.115304)
* [[A hybrid BFGS-Like method]] (see more https://doi.org/10.1016/j.cam.2024.115857)
* [[Conjugate gradient method]]s (see more https://doi.org/10.1016/j.jksus.2022.101923)