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Each problem takes a Boolean formula as input and the task is to compute the number of satisfying assignments. This can be further generalized by using larger domain sizes and attaching a weight to each satisfying assignment and computing the sum of these weights. It is known that any complex weighted #CSP problem is either in FP or #P-hard. [32]
A Sudoku may also be modelled as a constraint satisfaction problem. In his paper Sudoku as a Constraint Problem, [14] Helmut Simonis describes many reasoning algorithms based on constraints which can be applied to model and solve problems. Some constraint solvers include a method to model and solve Sudokus, and a program may require fewer than ...
Python is known as a glue language, [75] able to work very well with many other languages with ease of access. Python uses dynamic typing and a combination of reference counting and a cycle-detecting garbage collector for memory management. [76] It uses dynamic name resolution (late binding), which binds method and variable names during program ...
SymPy is an open-source Python library for symbolic computation. It provides computer algebra capabilities either as a standalone application, as a library to other applications, or live on the web as SymPy Live [2] or SymPy Gamma. [3] SymPy is simple to install and to inspect because it is written entirely in Python with few dependencies.
In computer science, the dining philosophers problem is an example problem often used in concurrent algorithm design to illustrate synchronization issues and techniques for resolving them. It was originally formulated in 1965 by Edsger Dijkstra as a student exam exercise, presented in terms of computers competing for access to tape drive ...
2-opt. In optimization, 2-opt is a simple local search algorithm for solving the traveling salesman problem.The 2-opt algorithm was first proposed by Croes in 1958, [1] although the basic move had already been suggested by Flood. [2]
The divide-and-conquer paradigm is often used to find an optimal solution of a problem. Its basic idea is to decompose a given problem into two or more similar, but simpler, subproblems, to solve them in turn, and to compose their solutions to solve the given problem. Problems of sufficient simplicity are solved directly.
The problem is used as a toy problem for computer science. It can be solved with an expert system such as CLIPS. The example set of rules that CLIPS provides is somewhat fragile in that naive changes to the rulebase that might seem to a human of average intelligence to make common sense can cause the engine to fail to get the monkey to reach ...