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A Kahn process network (KPN, or process network) is a distributed model of computation in which a group of deterministic sequential processes communicate through unbounded first in, first out channels. The model requires that reading from a channel is blocking while writing is non-blocking.
It is required to perform all tasks by assigning exactly one agent to each task in such a way that the maximum cost among the individual assignments is minimized. The term " bottleneck " is explained by a common type of application of the problem, where the cost is the duration of the task performed by an agent.
Karn's algorithm addresses the problem of getting accurate estimates of the round-trip time for messages when using the Transmission Control Protocol (TCP) in computer networking. The algorithm, also sometimes termed as the Karn-Partridge algorithm [ 1 ] was proposed in a paper by Phil Karn and Craig Partridge in 1987.
An alternative algorithm for topological sorting is based on depth-first search.The algorithm loops through each node of the graph, in an arbitrary order, initiating a depth-first search that terminates when it hits any node that has already been visited since the beginning of the topological sort or the node has no outgoing edges (i.e., a leaf node):
This algorithm can also be rewritten to use the Fast2Sum algorithm: [7] function KahanSum2(input) // Prepare the accumulator. var sum = 0.0 // A running compensation for lost low-order bits. var c = 0.0 // The array input has elements indexed for i = 1 to input.length do // c is zero the first time around.
In computer science and formal methods, a SAT solver is a computer program which aims to solve the Boolean satisfiability problem (SAT). On input a formula over Boolean variables, such as "(x or y) and (x or not y)", a SAT solver outputs whether the formula is satisfiable, meaning that there are possible values of x and y which make the formula true, or unsatisfiable, meaning that there are no ...
In applied mathematics, the maximum generalized assignment problem is a problem in combinatorial optimization. This problem is a generalization of the assignment problem in which both tasks and agents have a size. Moreover, the size of each task might vary from one agent to the other.
The following algorithm using that relaxation is an expected (1-1/e)-approximation: [10] Solve the linear program L and obtain a solution O; Set variable x to be true with probability y x where y x is the value given in O. This algorithm can also be derandomized using the method of conditional probabilities.