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Quantum approximate optimization algorithm (QAOA) can be employed to solve Knapsack problem using quantum computation by minimizing the Hamiltonian of the problem. The Knapsack Hamiltonian is constructed via embedding the constraint condition to the cost function of the problem with a penalty term. [29]
The knapsack problem can be solved by dynamic programming in pseudo-polynomial time: (), where m is the number of inputs and V is the number of different possible values. To get a polynomial-time algorithm, we can solve the knapsack problem approximately, using input rounding.
In the remaining case, the algorithm chooses x i = w i. Because of the need to sort the materials, this algorithm takes time O(n log n) on inputs with n materials. [1] [2] However, by adapting an algorithm for finding weighted medians, it is possible to solve the problem in time O(n). [2]
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. For this reason, many special cases and generalizations have been examined.
The hexagonal packing of circles on a 2-dimensional Euclidean plane. These problems are mathematically distinct from the ideas in the circle packing theorem.The related circle packing problem deals with packing circles, possibly of different sizes, on a surface, for instance the plane or a sphere.
The problem of fractional knapsack with penalties was introduced by Malaguti, Monaci, Paronuzzi and Pferschy. [44] They developed an FPTAS and a dynamic program for the problem, and they showed an extensive computational study comparing the performance of their models.
The separation oracle for the dual LP can be implemented by solving the knapsack problem with sizes s and values y: if the optimal solution of the knapsack problem has a total value at most 1, then y is feasible; if it is larger than 1, than y is not feasible, and the optimal solution of the knapsack problem identifies a configuration for which ...
Note: consider In the 2-weighted knapsack problem, where each item has two weights and a value, and the goal is to maximize the value such that the sum of squares of the total weights is at most the knapsack capacity: (,) + (,). We could solve it using a similar DP, where each state is (current weight 1, current weight 2, value).