Random Reshuffling (RR) is an algorithm for minimizing finite-sum functions that utilizes iterative gradient descent steps in conjunction with data reshuffling. Often contrasted with its sibling Stochastic Gradient Descent (SGD), RR is usually faster in practice and enjoys significant popularity in convex and non-convex optimization. The convergence rate of RR has attracted substantial attention recently and, for strongly convex and smooth functions, it was shown to converge faster than SGD if 1) the stepsize is small, 2) the gradients are bounded, and 3) the number of epochs is large. We remove these 3 assumptions, improve the dependence on the condition number from $\kappa^2$ to $\kappa$ (resp.\

The abstract claims to remove the small step size requirements of prior work. However, to attain a good convergence rate (Corollary 1) the main theorems (Theorems 1 and 2) need a small step size, similar to previous works. In fact Safran and Shamir (2020) show that convergence is only possible for step size O(1/n) . Please modify the claims accordingly. However, the dependence on \mu has worsened.

Random Reshuffling (RR) is an algorithm for minimizing finite-sum functions that utilizes iterative gradient descent steps in conjunction with data reshuffling. Often contrasted with its sibling Stochastic Gradient Descent (SGD), RR is usually faster in practice and enjoys significant popularity in convex and non-convex optimization. The convergence rate of RR has attracted substantial attention recently and, for strongly convex and smooth functions, it was shown to converge faster than SGD if 1) the stepsize is small, 2) the gradients are bounded, and 3) the number of epochs is large. We remove these 3 assumptions, improve the dependence on the condition number from \kappa 2 to \kappa (resp.\ We argue through theory and experiments that the new variance type gives an additional justification of the superior performance of RR.

Hot Wheels has excelled at merging the real and virtual worlds for the past few years, but a lot of that has really been focused on the driving experience. Specifically, how to make it more like a video game with toys like Hot Wheels AI, Mindracers and Augmoto. This year the brand is finally giving budding gear heads some love with its new TechMods set, an app-controlled vehicle that you build yourself and then control with your phone. It's not the same as tinkering under a hood, but it is actually fun to put together. The kit comes with a plastic chassis, which consists of a battery, a motor and four wheels, two of which can be steered. There's a plastic frame that snaps onto the top of it, with the rest of the vehicle's body made up of pre-cut plastic pieces that you punch out of several sheets and fold according to the in-app instructions.

Dolphins communicate through a range of unique clicks and whistles, and now scientists have created an algorithm that can decipher these calls.