• DR. M. HELEN SHEEBA

The basic issues found in the field of Artificial intelligence - structural risk minimization, which can be represented as the average of a large number of smooth component functions plus a simple and convex function. In this paper, we propose a novel adjacent variance reducing stochastic method building on the introduced NSRM2. Our method achieves two adjacent operator calculations by combining the fast Douglas–Rachford splitting and refers to the scheme of the FISTA algorithm in the choice of momentum factors. We show that the objective function value converges to the iteration point at the rate of O(1/k) when each loss function is convex and smooth. In addition, we prove that our method achieves a linear convergence rate for strongly convex and smooth loss functions. Experiments demonstrate the effectiveness of the proposed algorithm, especially when the loss function is ill-conditioned with good acceleration.

Adjacent operator; Composite optimization; Variance Reduction (VR); fast Douglas–Rachford (DR) splitting;

"The Approach of Stochastic Gradient Descent Method with Novel Structural Risk Adjacent Methodology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.d120-d129, August-2023, Available :http://www.jetir.org/papers/JETIR2308316.pdf

"The Approach of Stochastic Gradient Descent Method with Novel Structural Risk Adjacent Methodology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppd120-d129, August-2023, Available at : http://www.jetir.org/papers/JETIR2308316.pdf