Search results
Results from the WOW.Com Content Network
The STL was created as the first library of generic algorithms and data structures for C++, with four ideas in mind: generic programming, abstractness without loss of efficiency, the Von Neumann computation model, [2] and value semantics. The STL and the C++ Standard Library are two distinct entities. [3]
Until the standardization of the C++ language in 1998, they were part of the Standard Template Library (STL), published by SGI. Alexander Stepanov , the primary designer of the STL, bemoans the choice of the name vector , saying that it comes from the older programming languages Scheme and Lisp but is inconsistent with the mathematical meaning ...
The C++ Standard Library is based upon conventions introduced by the Standard Template Library (STL), and has been influenced by research in generic programming and developers of the STL such as Alexander Stepanov and Meng Lee. [4] [5] Although the C++ Standard Library and the STL share many features, neither is a strict superset of the other.
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.
Much of the C++ Standard Template Library (STL) makes heavy use of template-based function objects. Another way to create a function object in C++ is to define a non-explicit conversion function to a function pointer type, a function reference type, or a reference to function pointer type.
This parallels the extension of the scalar product of vector spaces to the direct sum above. The resulting abelian group is called the direct sum of G and H and is usually denoted by a plus symbol inside a circle: It is customary to write the elements of an ordered sum not as ordered pairs (g, h), but as a sum g + h.
The dynamic array has performance similar to an array, with the addition of new operations to add and remove elements: Getting or setting the value at a particular index (constant time) Iterating over the elements in order (linear time, good cache performance) Inserting or deleting an element in the middle of the array (linear time)
Pairwise summation is the default summation algorithm in NumPy [9] and the Julia technical-computing language, [10] where in both cases it was found to have comparable speed to naive summation (thanks to the use of a large base case).