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Is this problem a knapsack problem?

What further optimizations can be done to a Knapsack problem beyond a typical Dynamic Programming solution?

  • Given the weight of each item is at most 6% of the Capacity of the Knapsack. Can we do better than O(nC).

  • Answer:

    There are efficient approximation algorithms that work very well in practice. Here's an example of a hill climbing like technique: For a fixed order you can add the items one by one as long as they fit in the knapsack (this is called First Fit). Then you can apply some mutations to the solution like trying to swap one element in and one out. You can apply this on lots of randomized orders and return the best solution.,

Cosmin Negruseri at Quora Visit the source

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Use a Python dictionary  rather than a list of lists, because it's similarly efficient but can  handle double indexing better.  Also that way you can use the has_key   method  instead of having to initialize each entry with a flag value. For the nested loops: keep outer loop over  value of  items as each recursive call involves index number of   items. Replace recursive calls by dictionary lookups.

Sameer Gupta

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