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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.
As of June 2018, the Summit supercomputer held the top spot in the HPCG performance rankings, followed by the Sierra and the K computer. [7] In June of 2020, Summit was superseded by Fugaku with a speed of 16.0 HPCG-petaflops (an increase of 540%). Summit is currently 4th, [8] LUMI 3rd and Frontier 2nd.
Illustration of gradient descent on a series of level sets. 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 , ().
In optimization, a gradient method is an algorithm to solve problems of the form min x ∈ R n f ( x ) {\displaystyle \min _{x\in \mathbb {R} ^{n}}\;f(x)} with the search directions defined by the gradient of the function at the current point.
Production of images of a cross-section through an object by multiple X-ray measurements processed in a computer. computer engineering The profession of designing computers. computer hardware That part of a computer system with physical existence. computer programming The practice of producing instructions for a computer to achieve some desired ...
In computer engineering, instruction pipelining is a technique for implementing instruction-level parallelism within a single processor. Pipelining attempts to keep every part of the processor busy with some instruction by dividing incoming instructions into a series of sequential steps (the eponymous "pipeline") performed by different processor units with different parts of instructions ...
Its main feature is the gradient approximation that requires only two measurements of the objective function, regardless of the dimension of the optimization problem. Recall that we want to find the optimal control with loss function ():
In computer science, and more specifically in computability theory and computational complexity theory, a model of computation is a model which describes how an output of a mathematical function is computed given an input. A model describes how units of computations, memories, and communications are organized. [1]