Computer Scientists Discover Limits of Major Research Algorithm The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult ...

Unlike the metaphorical mountaineer, optimization researchers can program their gradient descent algorithms to take steps of any size. Giant leaps are tempting but also risky, as they could overshoot ...

However, the gradient descent algorithms need to update variables one by one to calculate the loss function with each iteration, which leads to a large amount of computation and a long training time.

To machine learning pioneer Terry Sejnowski, the mathematical technique called stochastic gradient descent is the “secret sauce” of deep learning, and most people don’t actually grasp its ...

Dr. James McCaffrey of Microsoft Research explains stochastic gradient descent (SGD) neural network training, specifically implementing a bio-inspired optimization technique called differential ...

The sparsity constrained rank-one matrix approximation problem is a difficult mathematical optimization problem which arises in a wide array of useful applications in engineering, machine learning, ...