variational problem - significado y definición. Qué es variational problem
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Qué (quién) es variational problem - definición

DIFFERENTIAL CALCULUS ON FUNCTION SPACES
Variational methods; Variational calculus; Variational Calculus; Minimum principle; Calculus of variation; Strong extrema; Variational Method; Calculus of Variations; Applications of the calculus of variations; Variational method; Variation calculus; Variational problem; Variational formulation

Calculus of variations         
The calculus of variations (or Variational Calculus) is a field of mathematical analysis that uses variations, which are small changes in functions
Variational Bayesian methods         
  • Pictorial illustration of coordinate ascent variational inference algorithm by the duality formula<ref name=Yoon2021/>
MATHEMATICAL METHODS USED IN BAYESIAN INFERENCE AND MACHINE LEARNING
Variational bayes; Variational Bayes; Variational Bayesian method; Variational inference; Variational free energy
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as might be described by a graphical model.
Variational autoencoder         
  • The scheme of the reparameterization trick. The randomness variable <math>{\varepsilon}</math> is injected into the latent space <math>z</math> as external input. In this way, it is possible to backpropagate the gradient without involving stochastic variable during the update.
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  • The basic scheme of a variational autoencoder. The model receives <math>x</math> as input. The encoder compresses it into the latent space. The decoder receives as input the information sampled from the latent space and produces <math>{x'}</math> as similar as possible to <math>x</math>.
DEEP LEARNING GENERATIVE MODEL TO ENCODE DATA REPRESENTATION
Variational autoencoders
In machine learning, a variational autoencoder (VAE), is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling, belonging to the families of probabilistic graphical models and variational Bayesian methods.

Wikipedia

Calculus of variations

The calculus of variations (or Variational Calculus) is a field of mathematical analysis that uses variations, which are small changes in functions and functionals, to find maxima and minima of functionals: mappings from a set of functions to the real numbers. Functionals are often expressed as definite integrals involving functions and their derivatives. Functions that maximize or minimize functionals may be found using the Euler–Lagrange equation of the calculus of variations.

A simple example of such a problem is to find the curve of shortest length connecting two points. If there are no constraints, the solution is a straight line between the points. However, if the curve is constrained to lie on a surface in space, then the solution is less obvious, and possibly many solutions may exist. Such solutions are known as geodesics. A related problem is posed by Fermat's principle: light follows the path of shortest optical length connecting two points, which depends upon the material of the medium. One corresponding concept in mechanics is the principle of least/stationary action.

Many important problems involve functions of several variables. Solutions of boundary value problems for the Laplace equation satisfy the Dirichlet's principle. Plateau's problem requires finding a surface of minimal area that spans a given contour in space: a solution can often be found by dipping a frame in soapy water. Although such experiments are relatively easy to perform, their mathematical formulation is far from simple: there may be more than one locally minimizing surface, and they may have non-trivial topology.