condition2 = np.logical_and(-8 < x, x <= -4.5) condition3 = np.logical_and(-4.5 < x, x <= -3) condition4 = np.logical_and(-3 < x, x <= -2) condition5 = np.logical_and ...

Abstract: We consider the problem of online sparse linear approximation, where a learner sequentially predicts the best sparse linear approximations of an as yet unobserved sequence of measurements in ...

Abstract: This paper studies the approximation, in least square sense, of a nonlinear system with a linear system, when the measurements are made with periodic signals with a high number of harmonics.

:param layers: A list of Pytorch layers containing only Linear/Conv2d/ReLU/ :param mode: the relaxation can be implemented in four ways. Solve the MIP to completion: "mip-exact". Solve the relaxation ...

When both variables are subject to error, a straight line may be fitted by minimizing the sum of squared distances of the observed points to the line. Approximate distributions of the slope of this ...

ABSTRACT: Nonlinear approximation is widely used in signal processing. Real-life signals can be modeled as functions of bounded variation. Thus the variable knot of approximating function could be ...

This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...

Rui Zhang, Saied Samiedaluie, and Dan Zhang. 2022. Product-based Approximate Linear Programs for Network Revenue Management. Operations Research, 70(5):2837-2850. The approximate linear programming ...