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[1] The subset sum problem is a special case of the decision and 0-1 problems where each kind of item, the weight equals the value: =. In the field of cryptography, the term knapsack problem is often used to refer specifically to the subset sum problem. The subset sum problem is one of Karp's 21 NP-complete problems.
The knapsack problem is one of the most studied problems in combinatorial optimization, with many real-life applications. For this reason, many special cases and generalizations have been examined. [1] [2] Common to all versions are a set of n items, with each item having an associated profit p j and weight w j.
The quadratic knapsack problem (QKP), first introduced in 19th century, [1] is an extension of knapsack problem that allows for quadratic terms in the objective function: Given a set of items, each with a weight, a value, and an extra profit that can be earned if two items are selected, determine the number of items to include in a collection without exceeding capacity of the knapsack, so as ...
0-1 knapsack problem. [19] Unbounded knapsack problem. [20] Multi-dimensional knapsack problem with Delta-modular constraints. [21] Multi-objective 0-1 knapsack problem. [22] Parametric knapsack problem. [23] Symmetric quadratic knapsack problem. [24] Count-subset-sum (#SubsetSum) - finding the number of distinct subsets with a sum of at most C ...
For example, bin packing is strongly NP-complete while the 0-1 Knapsack problem is only weakly NP-complete. Thus the version of bin packing where the object and bin sizes are integers bounded by a polynomial remains NP-complete, while the corresponding version of the Knapsack problem can be solved in pseudo-polynomial time by dynamic programming.
Coin values can be modeled by a set of n distinct positive integer values (whole numbers), arranged in increasing order as w 1 through w n.The problem is: given an amount W, also a positive integer, to find a set of non-negative (positive or zero) integers {x 1, x 2, ..., x n}, with each x j representing how often the coin with value w j is used, which minimize the total number of coins f(W)
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Using dynamic programming in the calculation of the nth member of the Fibonacci sequence improves its performance greatly. Here is a naïve implementation, based directly on the mathematical definition: function fib(n) if n <= 1 return n return fib(n − 1) + fib(n − 2)