Majorization-minimization algorithms consist of successively minimizing a sequence of
upper bounds of the objective function. These upper bounds are tight at the current estimate …

We introduce a fast iterative shrinkage algorithm for patch-smoothness regularization of
inverse problems in medical imaging. This approach is enabled by the reformulation of …

Nonlinear dimensionality reduction by manifold embedding has become a popular and
powerful approach both for visualization and as preprocessing for predictive tasks, but more …

The constrained estimation in Cox's model for the right-censored survival data is studied and
the asymptotic properties of the constrained estimators are derived by using the Lagrangian …

To model binomial data with large frequencies of both zeros and right-endpoints, Deng and
Zhang (in press) recently extended the zero-inflated binomial distribution to an endpoint …

本文研究了Newton-Raphson 等算法无法进行时探寻更加稳定的数值解法的问题. 利用Böhning
& Linday (1988) 提出的二次下界算法(Quadratic lower-bound), 文中在Logistic …

We show that proximal minimization algorithms (PMA), majorization minimization (MM), and
alternating minimization (AM) are equivalent. Each type of algorithm leads to a decreasing …

Sharp majorization is extended to the multivariate case. To achieve this, the notions of σ-
strong convexity, monotonicity, and one-sided Lipschitz continuity are extended to Σ-strong …

Deep neural networks (DNN) are typically optimized with stochastic gradient descent (SGD)
using a fixed learning rate or an adaptive learning rate approach (ADAGRAD). In this paper …

Ratings scales are ubiquitous in empirical research, especially in the social sciences, where
they are used for measuring abstract concepts such as opinion or attitude. Survey questions …