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The existence of a solution to a CSP can be viewed as a decision problem. This can be decided by finding a solution, or failing to find a solution after exhaustive search (stochastic algorithms typically never reach an exhaustive conclusion, while directed searches often do, on sufficiently small problems). In some cases the CSP might be known ...
algorithm MIN-CONFLICTS is input: console.csp, A constraint satisfaction problem. max_steps, The number of steps allowed before giving up. current_state, An initial assignment of values for the variables in the csp. output: A solution set of values for the variable or failure.
Two sets of overlapping data used to illustrate how CSP can separate the data. Two sets of data after rotation by CSP to maximize the ratio of the variances along the two axes. Common spatial pattern ( CSP ) is a mathematical procedure used in signal processing for separating a multivariate signal into additive subcomponents which have maximum ...
Cassowary constraint solver, an open source project for constraint satisfaction (accessible from C, Java, Python and other languages). Comet, a commercial programming language and toolkit; Gecode, an open source portable toolkit written in C++ developed as a production-quality and highly efficient implementation of a complete theoretical ...
In computer science, communicating sequential processes (CSP) is a formal language for describing patterns of interaction in concurrent systems. [1] It is a member of the family of mathematical theories of concurrency known as process algebras, or process calculi, based on message passing via channels.
The earlier AC algorithms are often considered too inefficient, and many of the later ones are difficult to implement, and so AC-3 is the one most often taught and used in very simple constraint solvers. The AC-3 algorithm is not to be confused with the similarly named A3C algorithm in machine learning. [1]
The main problem of these algorithms is the possible presence of plateaus, which are regions of the space of assignments where no local move decreases cost. The second class of local search algorithm have been invented to solve this problem. They escape these plateaus by doing random moves, and are called randomized local search algorithms.
For example, if is arc consistent with but the algorithm reduces the domain of , arc consistency of with does not hold any longer, and has to be enforced again. A simplistic algorithm would cycle over the pairs of variables, enforcing arc consistency, repeating the cycle until no domains change for a whole cycle.