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  2. Query optimization - Wikipedia

    en.wikipedia.org/wiki/Query_optimization

    Query optimization is a feature of many relational database management systems and other databases such as NoSQL and graph databases. The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans .

  3. Data conditioning - Wikipedia

    en.wikipedia.org/wiki/Data_conditioning

    Data conditioning is the use of data management and optimization techniques which result in the intelligent routing, optimization and protection of data for storage or data movement in a computer system.

  4. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    Robust optimization is, like stochastic programming, an attempt to capture uncertainty in the data underlying the optimization problem. Robust optimization aims to find solutions that are valid under all possible realizations of the uncertainties defined by an uncertainty set.

  5. List of optimization software - Wikipedia

    en.wikipedia.org/wiki/List_of_optimization_software

    The use of optimization software requires that the function f is defined in a suitable programming language and connected at compilation or run time to the optimization software. The optimization software will deliver input values in A , the software module realizing f will deliver the computed value f ( x ) and, in some cases, additional ...

  6. Database normalization - Wikipedia

    en.wikipedia.org/wiki/Database_normalization

    Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model .

  7. Regularization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Regularization_(mathematics)

    In machine learning, a key challenge is enabling models to accurately predict outcomes on unseen data, not just on familiar training data. Regularization is crucial for addressing overfitting—where a model memorizes training data details but can't generalize to new data. The goal of regularization is to encourage models to learn the broader ...

  8. Stochastic optimization - Wikipedia

    en.wikipedia.org/wiki/Stochastic_optimization

    Indeed, this randomization principle is known to be a simple and effective way to obtain algorithms with almost certain good performance uniformly across many data sets, for many sorts of problems. Stochastic optimization methods of this kind include: simulated annealing by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi (1983) [10] quantum annealing

  9. Jenks natural breaks optimization - Wikipedia

    en.wikipedia.org/wiki/Jenks_natural_breaks...

    The Jenks optimization method, also called the Jenks natural breaks classification method, is a data clustering method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class's average deviation from the class mean, while maximizing each class's deviation from the means of the ...

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