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Kahn process networks were originally developed for modeling parallel programs, but have proven convenient for modeling embedded systems, high-performance computing systems, signal processing systems, stream processing systems, dataflow programming languages, and other computational tasks. KPNs were introduced by Gilles Kahn in 1974. [1]
Neumaier [10] introduced an improved version of Kahan algorithm, which he calls an "improved Kahan–Babuška algorithm", which also covers the case when the next term to be added is larger in absolute value than the running sum, effectively swapping the role of what is large and what is small. In pseudocode, the algorithm is:
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.
Focuses on Kahn's role in the development of computer networking from 1967 through the early 1980s. Beginning with his work at Bolt Beranek and Newman (BBN), Kahn discusses his involvement as the ARPANET proposal was being written, his decision to become active in its implementation, and his role in the public demonstration of the ARPANET. The ...
In computer science, the ostrich algorithm is a strategy of ignoring potential problems on the basis that they may be exceedingly rare. It is named after the ostrich effect which is defined as "to stick one's head in the sand and pretend there is no problem". It is used when it appears the situation may be more cost-effectively managed by ...
The Kahn–Kalai conjecture, also known as the expectation threshold conjecture or more recently the Park-Pham Theorem, was a conjecture in the field of graph theory and statistical mechanics, proposed by Jeff Kahn and Gil Kalai in 2006. [1] [2] It was proven in a paper published in 2024. [3]
In mathematical optimization, the Karush–Kuhn–Tucker (KKT) conditions, also known as the Kuhn–Tucker conditions, are first derivative tests (sometimes called first-order necessary conditions) for a solution in nonlinear programming to be optimal, provided that some regularity conditions are satisfied.