Fast algorithms for matrix multiplication, namely those that perform asymptotically fewer scalar operations than the classical algorithm, have been c…

Inexact alternating direction multiplier methods (ADMMs) are developed for solving general separable convex optimization problems with a linear const…

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Kenne…

It is a well established result of linear theory that the influence of differing mechanical boundary conditions, i.e., stress-free or no-slip, on the…

The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with expos…

We review recent results obtained in the physics of the thermal Casimir force acting between two dielectrics, dielectric and metal, and between metal…

In this paper two types of multgrid methods, i.e., the Rayleigh quotient iteration and the inverse iteration with fixed shift, are developed for solv…

The present paper develops an optimal linear quadratic boundary controller for $2\times2$ linear hyperbolic partial differential equations (PDEs) wit…

We study locking of the modulation frequency of a relative periodic orbit in a general $S^1$-equivariant system of ordinary differential equations un…

Inspired by the construction of the Gribov-Zwanziger action in the Landau gauge, we introduce a quark model exhibiting both confinement and chiral sy…

This paper is concerned with inverse scattering of plane waves by a locally perturbed infinite plane (which is called a locally rough surface) with t…

Motivated by the emergence of randomized Krylov space methods for low-rank approximations [34, 43], we presented a ”proof of concept”, that is, structural results for the accuracy of approximate dominant subspaces.

This paper is concerned with approximating the dominant left singular vector space of a real matrix $A$ of arbitrary dimension, from block Krylov spa…

We develop a new proximal-gradient method for minimizing the sum of a differentiable, possibly nonconvex, function plus a convex, possibly non differ…

Procedures in assessing the impact of serial dependency on performance analysis are usually built on parametrically specified models. In this paper, …

For unconstrained control problems, a local convergence rate is established for an $hp$-method based on collocation at the Radau quadrature points in…

In this paper, two robust model predictive control (MPC) schemes are proposed for tracking control of nonholonomic systems with bounded disturbances:…

We design an asymptotic-preserving scheme for the semiconductor Boltzmann equation which leads to an energy-transport system for electron mass and in…

In this paper, using the linearization technique we write the Helmholtz transmission eigenvalue problem as an equivalent nonselfadjoint linear eigenv…

We give statistical guarantees for the sample average approximation (SAA) of stochastic optimization problems. Precisely, we derive exponential non-asymptotic finite-sample deviation inequalities for the approximate optimal solutions and optimal val…

We give statistical guarantees for the sample average approximation (SAA) of stochastic optimization problems. Precisely, we derive exponential non-a…

We introduce {\em vector diffusion maps} (VDM), a new mathematical framework for organizing and analyzing massive high dimensional data sets, images …

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