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!

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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