The respiratory and heart rates are critical physiological parameters, conventional contact-based monitoring techniques may cause discomfort epidermal damage, being therefore inadequate for long-term monitoring. Despite recent advances, accurate contactless vital sign is still challenging in practical scenarios, especially relation to rate estimation. In this work, we propose a comprehensive fr...

Abstract We introduce a general framework for large-scale model-based derivative-free optimization based on iterative minimization within random subspaces. present probabilistic worst-case complexity analysis our method, where in particular we prove high-probability bounds the number of iterations before given optimality is achieved. This specialized to nonlinear least-squares problems, with Ga...

We describe the design and computational performance of parallel row-oriented tensor algorithms for the solution of dense systems of nonlinear equations and nonlinear least squares problems on a distributed-memory MIMD multiprocessor. Tensor methods are general purpose methods that base each iteration upon a quadratic model of the nonlinear function, rather than the standard linear model, where...

We consider the problem of locating a user’s position from a set of noisy pseudoranges to a group of satellites. We consider both the nonlinear least squares formulation of the problem, which is nonconvex and nonsmooth, and the nonlinear squared least squares variant, in which the objective function is smooth, but still nonconvex. We show that the squared least squares problem can be reformulat...

We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of N...

The least median of squares estimator (Rousseeuw, 1984)1 of linear regression parameters is a high breakdown estimator, meaning that, unlike the least squares estimator, it performs reasonably well when up to 50% outliers are present in a data set. Unfortunately, it lacks efficiency under normal errors. This disadvantage can be overcome by using the least median of squares estimator as a starti...