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Each basis determines a unique BFS: for each basis B of m indices, there is at most one BFS with basis B. This is because x B {\displaystyle \mathbf {x_{B}} } must satisfy the constraint A B x B = b {\displaystyle A_{B}\mathbf {x_{B}} =b} , and by definition of basis the matrix A B {\displaystyle A_{B}} is non-singular, so the constraint has a ...
The variant where variables are required to be 0 or 1, called zero-one linear programming, and several other variants are also NP-complete [2] [3]: MP1 Some problems related to Job-shop scheduling; Knapsack problem, quadratic knapsack problem, and several variants [2] [3]: MP9 Some problems related to Multiprocessor scheduling
The pancake sorting problem and the problem to obtain the diameter of the pancake graph are equivalent. [ 16 ] The pancake graph of dimension n , P n can be constructed recursively from n copies of P n−1 , by assigning a different element from the set {1, 2, …, n} as a suffix to each copy.
The breadth-first-search algorithm is a way to explore the vertices of a graph layer by layer. It is a basic algorithm in graph theory which can be used as a part of other graph algorithms. For instance, BFS is used by Dinic's algorithm to find maximum flow in a graph.
Breadth-first search can be used to solve many problems in graph theory, for example: Copying garbage collection, Cheney's algorithm; Finding the shortest path between two nodes u and v, with path length measured by number of edges (an advantage over depth-first search) [14] (Reverse) Cuthill–McKee mesh numbering
In this decision problem, the input is a graph G and a number k; the desired output is yes if G contains a path of k or more edges, and no otherwise. [1] If the longest path problem could be solved in polynomial time, it could be used to solve this decision problem, by finding a longest path and then comparing its length to the number k ...
The maximum flow problem can be seen as a special case of more complex network flow problems, such as the circulation problem. The maximum value of an s-t flow (i.e., flow from source s to sink t) is equal to the minimum capacity of an s-t cut (i.e., cut severing s from t) in the network, as stated in the max-flow min-cut theorem .
The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [1] Haupt et al. [2] and from Rody Oldenhuis software. [3] Given the number of problems (55 in total), just a few are presented here. The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and ...