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This comparison of programming languages (array) compares the features of array data structures or matrix processing for various computer programming languages. Syntax [ edit ]
The third-party library ExtLib provides a polymorphic version of functional maps, called PMap, [12] which is given a comparison function upon creation. Lists of pairs and functional maps both provide a purely functional interface. By contrast, hash tables provide an imperative interface.
The Computer Language Benchmarks Game site warns against over-generalizing from benchmark data, but contains a large number of micro-benchmarks of reader-contributed code snippets, with an interface that generates various charts and tables comparing specific programming languages and types of tests.
Typically, two representations are present, one for integers fitting the native word size minus any tag bit (SmallInteger) and one supporting arbitrary sized integers (LargeInteger). Arithmetic operations support polymorphic arguments and return the result in the most appropriate compact representation.
uBLAS is a C++ template class library that provides BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices. Dlib: Davis E. King C++ 2006 19.24.2 / 05.2023 Free Boost C++ template library; binds to optimized BLAS such as the Intel MKL; Includes matrix decompositions, non-linear solvers, and machine learning tooling Eigen: Benoît ...
Hermes Project: C++/Python library for rapid prototyping of space- and space-time adaptive hp-FEM solvers. IML++ is a C++ library for solving linear systems of equations, capable of dealing with dense, sparse, and distributed matrices. IT++ is a C++ library for linear algebra (matrices and vectors), signal processing and communications ...
This comparison of programming languages compares how object-oriented programming languages such as C++, Java, Smalltalk, Object Pascal, Perl, Python, and others manipulate data structures. Object construction and destruction
Take the sequence of matrices and separate it into two subsequences. Find the minimum cost of multiplying out each subsequence. Add these costs together, and add in the cost of multiplying the two result matrices. Do this for each possible position at which the sequence of matrices can be split, and take the minimum over all of them.