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Matrix types (special types like bidiagonal/tridiagonal are not listed): Real – general (nonsymmetric) real; Complex – general (nonsymmetric) complex; SPD – symmetric positive definite (real)
The C++ Standard Library provides several generic containers, functions to use and manipulate these containers, function objects, generic strings and streams (including interactive and file I/O), support for some language features, and functions for common tasks such as finding the square root of a number.
Instances of such classes are called functors or function objects. Functors allow the behavior of the associated function to be parameterized (e.g. through arguments passed to the functor's constructor) and can be used to keep associated per-functor state information along with the function. Since both functors and function pointers can be ...
Basic Linear Algebra Subprograms (BLAS) is a specification that prescribes a set of low-level routines for performing common linear algebra operations such as vector addition, scalar multiplication, dot products, linear combinations, and matrix multiplication.
Julia has the vec(A) function as well. In Python NumPy arrays implement the flatten method, [ note 1 ] while in R the desired effect can be achieved via the c() or as.vector() functions. In R , function vec() of package 'ks' allows vectorization and function vech() implemented in both packages 'ks' and 'sn' allows half-vectorization.
[10] [11] vector<bool> does not meet the requirements for a C++ Standard Library container. For instance, a container<T>::reference must be a true lvalue of type T. This is not the case with vector<bool>::reference, which is a proxy class convertible to bool. [12] Similarly, the vector<bool>::iterator does not yield a bool& when dereferenced.
Vector projection, also known as vector resolute or vector component, a linear mapping producing a vector parallel to a second vector; Vector-valued function, a function that has a vector space as a codomain; Vectorization (mathematics), a linear transformation that converts a matrix into a column vector
Radial basis function (RBF) interpolation is an advanced method in approximation theory for constructing high-order accurate interpolants of unstructured data, possibly in high-dimensional spaces. The interpolant takes the form of a weighted sum of radial basis functions .