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cs.LG2023
Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization
Naoki Sato, Hideaki Iiduka
The graduated optimization approach is a method for finding global optimal solutions for nonconvex functions by using a function smoothing operation with stochastic noise. This pap…
cs.LG2023
Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo Line Search
Yuki Tsukada, Hideaki Iiduka
While stochastic gradient descent (SGD) can use various learning rates, such as constant or diminishing rates, the previous numerical results showed that SGD performs better than o…
math.NA2023
Modified memoryless spectral-scaling Broyden family on Riemannian manifolds
Hiroyuki Sakai, Hideaki Iiduka
This paper presents modified memoryless quasi-Newton methods based on the spectral-scaling Broyden family on Riemannian manifolds. The method involves adding one parameter to the s…