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  2. Discrete optimization - Wikipedia

    en.wikipedia.org/wiki/Discrete_optimization

    Discrete optimization is a branch of optimization in applied mathematics and computer science. As opposed to continuous optimization , some or all of the variables used in a discrete optimization problem are restricted to be discrete variables —that is, to assume only a discrete set of values, such as the integers .

  3. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. [1] [2] It is generally divided into two subfields: discrete optimization and continuous optimization.

  4. Combinatorial optimization - Wikipedia

    en.wikipedia.org/wiki/Combinatorial_optimization

    A minimum spanning tree of a weighted planar graph.Finding a minimum spanning tree is a common problem involving combinatorial optimization. Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, [1] where the set of feasible solutions is discrete or can be reduced to a discrete set.

  5. Simulated annealing - Wikipedia

    en.wikipedia.org/wiki/Simulated_annealing

    Stochastic optimization is an umbrella set of methods that includes simulated annealing and numerous other approaches. Particle swarm optimization is an algorithm modeled on swarm intelligence that finds a solution to an optimization problem in a search space, or models and predicts social behavior in the presence of objectives.

  6. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    In 1997, the practical performance benefits from vectorization achievable with such small batches were first explored, [13] paving the way for efficient optimization in machine learning. As of 2023, this mini-batch approach remains the norm for training neural networks, balancing the benefits of stochastic gradient descent with gradient descent .

  7. Category:Optimization algorithms and methods - Wikipedia

    en.wikipedia.org/wiki/Category:Optimization...

    Learning rate; Least squares; Least-squares spectral analysis; Lemke's algorithm; Level-set method; Levenberg–Marquardt algorithm; Lexicographic max-min optimization; Lexicographic optimization; Limited-memory BFGS; Line search; Linear-fractional programming; Lloyd's algorithm; Local convergence; Local search (optimization) Luus–Jaakola

  8. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3]

  9. Ant colony optimization algorithms - Wikipedia

    en.wikipedia.org/wiki/Ant_colony_optimization...

    Reactive search optimization Focuses on combining machine learning with optimization, by adding an internal feedback loop to self-tune the free parameters of an algorithm to the characteristics of the problem, of the instance, and of the local situation around the current solution. Tabu search (TS)