Jun 9, 2016 · In this paper, we consider the problem of least mean squares regression, in which a d- dimensional unknown parameter is desired to be estimated ...
May 19, 2017 · A stochastic gradient descent based algorithm with weighted iterate-averaging that uses a single pass over the data is studied and its convergence rate is ...
The SGD with averaging and constant step size scheme proposed in [29] for a linear least squares regression problem requires a fixed batch sample size as ...
A stochastic gradient descent based algorithm with weighted iterate-averaging that uses a single pass over the data is studied and its convergence rate is ...
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On projected stochastic gradient descent algorithm with weighted averaging for least squares regression Available Online ...
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization ...
Aug 3, 2022 · This exercise is to understand on how these two methods help solving coefficients in linear regression, and compare the output with least square solution.
Apr 19, 2021 · Projected gradient descent is appropriate for optimization problems that involve constraints on the solution.
Jul 18, 2023 · Various averaging schemes have been proposed to accelerate the convergence of SGD in different settings.
This paper considers stochastic subgradient mirror-descent method for solving constrained convex minimization problems.