Shape restrictions such as monotonicity on functions often arise naturally in statistical modeling. We consider a Bayesian approach to the estimation of monotone regression function and testing for monotonicity. construct prior distribution using piecewise constant functions. For estimation, imposing heights these steps is sensible, but resulting posterior harder analyze theoretically. “project...

Most transformational linguists would no longer create explicit deep structures. Instead they adopt a surface-tnterpretlve approach. We find deep structures indIspensable for projection into a semantic network. In conjunction wrth a reference architecture based on constraint-posting, they mlnlmlze referential non-determinism% We extend Marcus’ Determm~sm Hypothesis to include rmmed/ate referenc...

In blocking artifacts reduction based on the projection onto convex sets (POCS) technique, good constraint sets are very important. Until recently, smoothness constraint sets (SCS) are often formulated in the image domain, whereas quantization constraint set is defined in the block-based discrete cosine transform (BDCT) domain. Thus, frequent BDCT transform is inevitable in alternative projecti...

This paper focuses on convex constrained optimization problems, where the solution is subject to a convex inequality constraint. In particular, we aim at challenging problems for which both projection into the constrained domain and a linear optimization under the inequality constraint are time-consuming, which render both projected gradient methods and conditional gradient methods (a.k.a. the ...

Submitted to the Journal of Computational Physics Seven schemes to maintain the r B = 0 constraint numerically are compared. All these algorithms can be combined with shock-capturing Godunov type base schemes. They fall into three categories: the eight-wave formulation maintains the constraint to truncation error, the projection scheme enforces the constraint in some discretization by projectin...

Perspective Alignment is a novel method of solving backprojection, the well-known problem of computing threedimensional (3D) position and orientation (pose) of a model from two-dimensional (2D) image features. This paper demonstrates that previous back-projection methods can violate the visibility constraint by computing solution poses in which the model occludes features which should be visibl...

We consider convex optimization problems with structures that are suitable for stochastic sampling. In particular, we focus on problems where the objective function is an expected value or is a sum of a large number of component functions, and the constraint set is the intersection of a large number of simpler sets. We propose an algorithmic framework for projection-proximal methods using rando...