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Using a CP (Constraint Programming) solver guarantees an optimal solution. This is deterministic since the solve heuristic ("split") always give the same first result. |
Using a CP (Constraint Programming) solver guarantees an optimal solution. This is deterministic since the solve heuristic ("split") always give the same first result. |
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<lang Picat> |
<lang Picat>import cp. |
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go => |
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Words = ["abracadabra", |
Words = ["abracadabra", |
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"seesaw", |
"seesaw", |
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end.</lang> |
end.</lang> |
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{{out}} |
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Output: |
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<pre>abracadabra, baabacadrar, (0) |
<pre>abracadabra, baabacadrar, (0) |
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seesaw, assewe, (0) |
seesaw, assewe, (0) |
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aaaaaaa, aaaaaaa, (7)</pre> |
aaaaaaa, aaaaaaa, (7)</pre> |
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===All optimal solutions=== |
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Using a constraint solver makes it quite easy to generate all optimal solutions. |
Using a constraint solver makes it quite easy to generate all optimal solutions. |
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<lang Picat>go2 ?=> |
<lang Picat>go2 ?=> |
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go2 => true.</lang> |
go2 => true.</lang> |
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{{out}} |
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Output: |
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<pre>word = abracadabra |
<pre>word = abracadabra |
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best_score = 0 |
best_score = 0 |
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num_solutions = 1 |
num_solutions = 1 |
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solutions = [aaaaaaa]</pre> |
solutions = [aaaaaaa]</pre> |
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=={{header|PicoLisp}}== |
=={{header|PicoLisp}}== |
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