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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.
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.
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 .
The optimization problem itself is known to be NP-hard, ... With this definition, uniform random data should tend to have values near to 0.5, and clustered data ...
In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover" [1]) is a technique designed for problem solving more quickly when classic methods are too slow for finding an exact or approximate solution, or when classic methods fail to find any exact solution in a search space.
Generally data structures are more difficult to change than algorithms, as a data structure assumption and its performance assumptions are used throughout the program, though this can be minimized by the use of abstract data types in function definitions, and keeping the concrete data structure definitions restricted to a few places.
Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization , in which an object such as an integer , permutation or graph must be found from a countable set .
Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. [4] The objective function takes a set of hyperparameters and returns the associated loss. [ 4 ]