Shallow backtracking - definição. O que é Shallow backtracking. Significado, conceito
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O que (quem) é Shallow backtracking - definição

ALGORITHM
Backtracking search; Back tracking; Backtracking algorithm; Applications of backtracking algorithms
  • A [[Sudoku]] solved by backtracking.

backtracking         
<algorithm> A scheme for solving a series of sub-problems each of which may have multiple possible solutions and where the solution chosen for one sub-problem may affect the possible solutions of later sub-problems. To solve the overall problem, we find a solution to the first sub-problem and then attempt to recursively solve the other sub-problems based on this first solution. If we cannot, or we want all possible solutions, we backtrack and try the next possible solution to the first sub-problem and so on. Backtracking terminates when there are no more solutions to the first sub-problem. This is the algorithm used by logic programming languages such as Prolog to find all possible ways of proving a goal. An optimisation known as "intelligent backtracking" keeps track of the dependencies between sub-problems and only re-solves those which depend on an earlier solution which has changed. Backtracking is one algorithm which can be used to implement nondeterminism. It is effectively a depth-first search of a problem space. (1995-04-13)
backtracking         
Backtracking         
Backtracking is a class of algorithm for finding solutions to some computational problems, notably constraint satisfaction problems, that incrementally builds candidates to the solutions, and abandons a candidate ("backtracks") as soon as it determines that the candidate cannot possibly be completed to a valid solution.

Wikipédia

Backtracking

Backtracking is a class of algorithms for finding solutions to some computational problems, notably constraint satisfaction problems, that incrementally builds candidates to the solutions, and abandons a candidate ("backtracks") as soon as it determines that the candidate cannot possibly be completed to a valid solution.

The classic textbook example of the use of backtracking is the eight queens puzzle, that asks for all arrangements of eight chess queens on a standard chessboard so that no queen attacks any other. In the common backtracking approach, the partial candidates are arrangements of k queens in the first k rows of the board, all in different rows and columns. Any partial solution that contains two mutually attacking queens can be abandoned.

Backtracking can be applied only for problems which admit the concept of a "partial candidate solution" and a relatively quick test of whether it can possibly be completed to a valid solution. It is useless, for example, for locating a given value in an unordered table. When it is applicable, however, backtracking is often much faster than brute-force enumeration of all complete candidates, since it can eliminate many candidates with a single test.

Backtracking is an important tool for solving constraint satisfaction problems, such as crosswords, verbal arithmetic, Sudoku, and many other puzzles. It is often the most convenient technique for parsing, for the knapsack problem and other combinatorial optimization problems. It is also the basis of the so-called logic programming languages such as Icon, Planner and Prolog.

Backtracking depends on user-given "black box procedures" that define the problem to be solved, the nature of the partial candidates, and how they are extended into complete candidates. It is therefore a metaheuristic rather than a specific algorithm – although, unlike many other meta-heuristics, it is guaranteed to find all solutions to a finite problem in a bounded amount of time.

The term "backtrack" was coined by American mathematician D. H. Lehmer in the 1950s. The pioneer string-processing language SNOBOL (1962) may have been the first to provide a built-in general backtracking facility.