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The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems. GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search . [ 1 ]
Pages in category "Free software programmed in C++" The following 200 pages are in this category, out of approximately 545 total. This list may not reflect recent changes .
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 ...
Free and open-source software portal This category is for toolkits and libraries for application programmers which are distributed as free software - under a free software license , with the source code available.
In decision tree learning, greedy algorithms are commonly used, however they are not guaranteed to find the optimal solution. One popular such algorithm is the ID3 algorithm for decision tree construction. Dijkstra's algorithm and the related A* search algorithm are verifiably optimal greedy algorithms for graph search and shortest path finding.
The libraries are aimed at a wide range of C++ users and application domains. They range from general-purpose libraries like the smart pointer library, to operating system abstractions like Boost FileSystem, to libraries primarily aimed at other library developers and advanced C++ users, like the template metaprogramming (MPL) and domain-specific language (DSL) creation (Proto).
This category is for programming libraries written in and for the C++ programming language. For libraries written for the C programming language, see Category:C (programming language) libraries . Contents
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.