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Generally, the query optimizer cannot be accessed directly by users: once queries are submitted to the database server, and parsed by the parser, they are then passed to the query optimizer where optimization occurs. [2] [3] However, some database engines allow guiding the query optimizer with hints.
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The NAG Library [1] can be accessed from a variety of languages and environments such as C/C++, [2] Fortran, [3] Python, [4] AD, [5] MATLAB, [6] Java [7] and .NET. [8] The main supported systems are currently Windows, Linux and macOS running on x86-64 architectures; 32-bit Windows support is being phased out. Some NAG mathematical optimization ...
Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
In Python 3.x the range() function [28] returns a generator which computes elements of the list on demand. Elements are only generated when they are needed (e.g., when print(r[3]) is evaluated in the following example), so this is an example of lazy or deferred evaluation: >>>
Used in Python 2.3 and up, and Java SE 7. Insertion sorts Insertion sort: determine where the current item belongs in the list of sorted ones, and insert it there; Library sort; Patience sorting; Shell sort: an attempt to improve insertion sort; Tree sort (binary tree sort): build binary tree, then traverse it to create sorted list