.png){kind=icon}
.png){kind=icon}
Return
Multidimensional Gains for Stochastic Approximation
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2019.2920930.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper deals with iterative Jacobian-based recursion technique for the root-finding problem of the vector-valued function, whose evaluations are contaminated by noise. Instead of a scalar step size, we use an iterate-dependent matrix gain to effectively weigh the different elements associated with the noisy observations. The analytical development of the matrix gain is built on an iterative-dependent linear function interfered by additive zero-mean white noise, where the dimension of the function is $ {M\ge 1}$ and the dimension of the unknown variable is $ {N\ge 1}$ . Necessary and sufficient conditions for $ {M\ge N}$ algorithms are presented pertaining to algorithm stability and convergence of the estimate error covariance matrix. Two algorithms are proposed: one for the case where $ {M\ge N}$ and the second one for the antithesis. The two algorithms assume full knowledge of the Jacobian. The recursive algorithms are proposed for generating the optimal iterative-dependent matrix gain. The proposed algorithms here aim for per-iteration minimization of the mean square estimate error. We show that the proposed algorithm satisfies the presented conditions for stability and convergence of the covariance. In addition, the convergence rate of the estimation error covariance is shown to be inversely proportional to the number of iterations. For the antithesis $ {M< N}$ , contraction of the error covariance is guaranteed. This underdetermined system of equations can be helpful in training neural networks. Numerical examples are presented to illustrate the performance capabilities of the proposed multidimensional gain while considering nonlinear functions.
Keywords:
Convergence
Jacobian matrices
Noise measurement
Covariance matrices
Approximation algorithms
Estimation
Optimization
Gradient descent
Newton's method
noisy function measurements
root finding
Rosenbrock function
stochastic approximation (SA)
stochastic optimization
variance reduction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
7.5K
Citations:
7.2W