Search results
Results From The WOW.Com Content Network
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.
OpenMDAO is an open-source high-performance computing platform for systems analysis and multidisciplinary optimization written in the Python programming language.. The OpenMDAO project is primarily focused on supporting gradient based optimization with analytic derivatives to allow you to explore large design spaces with hundreds or thousands of design variables, but the framework also has a ...
The number of gradient descent iterations is commonly proportional to the spectral condition number of the system matrix (the ratio of the maximum to minimum eigenvalues of ), while the convergence of conjugate gradient method is typically determined by a square root of the condition number, i.e., is much faster.
Gradient method — method that uses the gradient as the search direction Gradient descent. Stochastic gradient descent; Landweber iteration — mainly used for ill-posed problems; Successive linear programming (SLP) — replace problem by a linear programming problem, solve that, and repeat
The C++ heyoka and python package heyoka.py make large use of this technique to offer advanced differentiable programming capabilities (also at high orders). A package for the Julia programming language – Zygote – works directly on Julia's intermediate representation. [7] [11] [5]
Main page; Contents; Current events; Random article; About Wikipedia; Contact us
Proximal gradient methods provide a general framework which is applicable to a wide variety of problems in statistical learning theory. Certain problems in learning can often involve data which has additional structure that is known a priori. In the past several years there have been new developments which incorporate information about group ...
Ordination or gradient analysis, in multivariate analysis, is a method complementary to data clustering, and used mainly in exploratory data analysis (rather than in hypothesis testing). In contrast to cluster analysis, ordination orders quantities in a (usually lower-dimensional) latent space. In the ordination space, quantities that are near ...