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In computer science, communicating sequential processes (CSP) is a formal language for describing patterns of interaction in concurrent systems. [1] It is a member of the family of mathematical theories of concurrency known as process algebras, or process calculi , based on message passing via channels .
Intel computer systems (and others) use this technology to reach effective FSB speeds of 1600 MT/s (million transfers per second), even though the FSB clock speed is only 400 MHz (cycles per second). A phase-locked loop in the CPU then multiplies the FSB clock by a factor in order to get the CPU speed.
Gradient descent is based on the observation that if the multi-variable function is defined and differentiable in a neighborhood of a point , then () decreases fastest if one goes from in the direction of the negative gradient of at , (). It follows that, if
Communicating sequential processes (CSP), developed by Tony Hoare, is a formal language that arose at a similar time to CCS.; The Algebra of Communicating Processes (ACP) was developed by Jan Bergstra and Jan Willem Klop in 1982, and uses an axiomatic approach (in the style of Universal algebra) to reason about a similar class of processes as CCS.
In network science, a gradient network is a directed subnetwork of an undirected "substrate" network where each node has an associated scalar potential and one out-link that points to the node with the smallest (or largest) potential in its neighborhood, defined as the union of itself and its neighbors on the substrate network.
Stochastic gradient descent competes with the L-BFGS algorithm, [citation needed] which is also widely used. Stochastic gradient descent has been used since at least 1960 for training linear regression models, originally under the name ADALINE. [25] Another stochastic gradient descent algorithm is the least mean squares (LMS) adaptive filter.
The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. [1] It has applications in geophysics, seismic imaging, photonics and more recently in neural networks. [2] The adjoint state space is chosen to simplify the physical interpretation of equation ...
Conjugate gradient, assuming exact arithmetic, converges in at most n steps, where n is the size of the matrix of the system (here n = 2). In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is positive-semidefinite.