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In a programming language, an evaluation strategy is a set of rules for evaluating expressions. [1] The term is often used to refer to the more specific notion of a parameter-passing strategy [2] that defines the kind of value that is passed to the function for each parameter (the binding strategy) [3] and whether to evaluate the parameters of a function call, and if so in what order (the ...
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:
Interpreting the universe of data types as a category , with morphisms being functions, then a type constructor F that is a member of the Functor type class is the object part of such a functor, and fmap :: (a -> b) -> F a -> F b is the morphism part. The functor laws described above are precisely the category-theoretic functor axioms for this ...
Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list.
In mathematics and computer science, Horner's method (or Horner's scheme) is an algorithm for polynomial evaluation.Although named after William George Horner, this method is much older, as it has been attributed to Joseph-Louis Lagrange by Horner himself, and can be traced back many hundreds of years to Chinese and Persian mathematicians. [1]
Under lazy evaluation, the length function returns the value 4 (i.e., the number of items in the list), since evaluating it does not attempt to evaluate the terms making up the list. In brief, strict evaluation always fully evaluates function arguments before invoking the function.
The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and Binh. [6] The software developed by Deb can be downloaded, [7] which implements the NSGA-II procedure with GAs, or the program posted on Internet, [8] which implements the NSGA-II procedure with ES.
Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [19]Probabilistic numerics have also been studied for mathematical optimization, which consist of finding the minimum or maximum of some objective function given (possibly noisy or indirect) evaluations of that function at a set of points.