Results 221 to 230 of about 76,322 (245)

Ultrasound Displacement Tracking Techniques for Post-Stroke Myofascial Shear Strain Quantification. [PDF]

IEEE Trans Biomed Eng
Ashikuzzaman M, Huang J, Bonwit S, Etemadimanesh A, Ghasemi A, Debs P, Nickl R, Enslein J, Fayad LM, Raghavan P, Bell MAL.   +10 more
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Diffusion stochastic optimization with non-smooth regularizers

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
We develop an effective distributed strategy for seeking the Pareto solution of an aggregate cost consisting of regularized risks. The focus is on stochastic optimization problems where each risk function is expressed as the expectation of some loss function and the probability distribution of the data is unknown.
Stefan Vlaski, Lieven Vandenberghe, Ali H. Sayed   +2 more
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Genetic Algorithms:“Non-Smooth” Discrete Optimization

1997
In Lesson 7, we described an algorithm (called simulated annealing) that solves “almost smooth” discrete optimization problems, i.e., problems in which a “small” change in the point x leads to a small change in the value of the objective function J(x). In this lesson, we consider “non-smooth” discrete optimization problems.
Hung T. Nguyen, Vladik Kreinovich
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New global optimization method for non-smooth unconstrained continuous optimization

AIP Conference Proceedings, 2017
In this paper, we propose a new global optimization algorithm for non-smooth unconstrained optimization problems. We construct a new global optimization (GO) method based on the function new smoothing technique. We give some numerical examples in order to demonstrate the effectiveness of our algorithm.
Nurullah Yilmaz, Ahmet Sahiner
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Optimality conditions for non-smooth semi-infinite programming

Optimization, 2010
This article deals with a class of non-smooth semi-infinite programming (SIP) problems in which the index set of the inequality constraints is an arbitrary set not necessarily finite. We introduce several kinds of constraint qualifications for these non-smooth SIP problems and we study the relationships between them.
N. Kanzi, S. Nobakhtian
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Optimal kernels when estimating non-smooth densities

Communications in Statistics - Theory and Methods, 1987
The derivation of new kernel functions for the kernel estimator of an unknown density function is given. These kernels are shown to be optimal in some sense when the underlying density f is continuous but its derivative f′ is not, and consequently a solu tion is presented for an unsolved problem which was stated by van Eeden (1985).
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