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Boggle Solver
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Boggle Solver
A BOGGLE SOLVER. Author: [email protected] (Anthony Yim) Status: In progress. This is an experiment to investigate the various ways of implementing a boggle solver. The goal is to figure out the most efficient (in terms of time complexity) and elegant way of solving boggle. For this experiment, we are not too concerned with memory limitations (space complexity). The following are ways I will solve Boggle for this experiment: 1. Compute all possible combination of letters by recusively backtracking. 2. Computer all possible combination of letters by recursively backtracking and pruning (do not continue to recurse if combo of letters will not lead to a word). 2a. Use a hash table of prefixes and words. 2b. Use a prefix tree (trie tree) - more memory efficient than a hash table of prefixes and words. Think you can do better? Email me! ***************************************************************** Sample output: 000000 0cnlw0 0zgul0 0lvzy0 0eglz0 000000 c: nzg n: clzgu l: nwgul w: lul z: cnglv g: cnlzulvz u: nlwglvzy l: lwuzy l: zgveg v: zgulzegl z: gulvyglz y: ulzlz e: lvg g: lvzel l: vzygz z: zyl Begin solving using hashmap of words... Answer: null Answer: null Answer: lung Answer: lug Answer: luv Answer: gnu Answer: gun Answer: gul Answer: gull Answer: gully Answer: gul Answer: gull Answer: guv Answer: guy Answer: gleg Answer: glegly Answer: lung Answer: lug Answer: luv Answer: lev Answer: leg Answer: vug Answer: veg Answer: gel Finished solving. Num of iterations: 12029640 Dictionary size: 172820 Begin solving using hashmap of prefixes and words (w/ pruning)... Answer: null Answer: null Answer: lung Answer: lug Answer: luv Answer: gnu Answer: gun Answer: gul Answer: gull Answer: gully Answer: gul Answer: gull Answer: guv Answer: guy Answer: gleg Answer: glegly Answer: lung Answer: lug Answer: luv Answer: lev Answer: leg Answer: vug Answer: veg Answer: gel Finished solving. Num of iters: 637 Dictionary size: 387880
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