Motivated by the smoothing approaches, we modify the classical bi-level gradient sequential averaging method to solve the bi-level optimization problem. Under some mild conditions, we obtain the ...

Value-at-risk (VaR) is a statistical method for judging the potential losses ... its volatility is calculated using a matrix. A variance-covariance matrix is computed for all the assets.

This capability makes it particularly well-suited for gradient-based inverse design ... In evaluating the performance of various transfer matrix method implementations, we conducted rigorous ...

Based on the secant condition often satisfied by quasi-Newton methods, two new versions of the Hestenes-Stiefel (HS) nonlinear conjugate gradient method are proposed, which are descent methods even ...

Abstract: Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous ...

This singles out our natural-gradient approach from other second-order-like methods as a particularly appealing framework for biological learning. These qualitative matches of experimental data to the ...

This new formulation is scalable as the low-rank matrix is factorized to the multiplication of two much smaller matrices. We then propose an alternating manifold proximal gradient continuation method ...

In 1981 Arnold Schönhage used this approach to prove that it’s possible to perform matrix multiplication in n 2.522 steps. Strassen later called this approach the “laser method.” Over the last few ...