An Accelerated Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization

We consider the problem of minimizing the sum of two convex functions: one is smooth and given by a gradient oracle, and the other is separable over blocks of coordinates and has a simple known structure over each block. We develop an accelerated randomized proximal coordinate gradient (APCG) method for minimizing such convex composite functions. For strongly convex functions, our method achieves faster linear convergence rates than existing randomized proximal coordinate gradient methods. Without strong convexity, our method enjoys accelerated sublinear convergence rates. We show how to apply the APCG method to solve the regularized empirical risk minimization (ERM) problem, and devise efficient implementations that avoid full-dimensional vector operations. For ill-conditioned ERM problems, our method obtains improved convergence rates than the state-of-the-art stochastic dual coordinate ascent (SDCA) method

Pointwise Approximation Theorems for Combinations of Bernstein Polynomials With Inner Singularities

We give direct and inverse theorems for the weighted approximation of functions with inner singularities by combinations of Bernstein polynomials.Comment: 13 pages, late

Efficient and flexible generation of entangled qudits with cross phase modulation

In this paper, we provide a simple but powerful module to generate entangled qudits. This module assisted with cross-Kerr nonlinearity is available to the entangled qudits generation with arbitrary dimension, and it could work well even when the two independent qudits lose the same number of single photons. Moreover, with the cascade uses of modules, the deterministic generation but with nonidentical forms of entangled qudits is possible.Comment: 2 figures, accepted versio