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Backtracking is a systematic method of trying out different decision sequences until a viable solution is found. It is useful for solving constraints-satisfying problems like puzzles or scheduling issues. Backtracking algorithms follow a depth-first search (DFS) pattern, extending partial solutions step by step and retracting if they lead to a constraint violation or dead end. It also eliminates multiple options with a single test, significantly reducing search space. Backtracking is used in various fields like puzzle solving, machine learning algorithms, and optimization problems. The algorithm can be structured as choose, constraint, goal check, continue or backtrack.
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