New Smoothing Approximations to Piecewise Smooth Functions and Applications
Sign Up to like & getrecommendations! Published in 2019 at "Numerical Functional Analysis and Optimization"
DOI: 10.1080/01630563.2018.1561466
Abstract: Abstract In this study, we present a new method of smoothing to approximate piecewise smooth functions. First, we give a new definition for piecewise smooth functions. Then, we present new local and global smoothing approximations… read more here.
Keywords: new smoothing; approximations piecewise; smooth functions; functions applications ... See more keywords
Optimization of non-smooth functions via differentiable surrogates
Sign Up to like & getrecommendations! Published in 2025 at "PLOS One"
DOI: 10.1371/journal.pone.0321862
Abstract: Mathematical optimization is fundamental across many scientific and engineering applications. While data-driven models like gradient boosting and random forests excel at prediction tasks, they often lack mathematical regularity, being non-differentiable or even discontinuous. These models… read more here.
Keywords: non smooth; prediction; functions via; smooth functions ... See more keywords
Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units
Sign Up to like & getrecommendations! Published in 2020 at "Communications in Computational Physics"
DOI: 10.4208/cicp.oa-2019-0168
Abstract: Deep neural networks with rectified linear units (ReLU) are getting more and more popular due to their universal representation power and successful applications. Some theoretical progress regarding the approximation power of deep ReLU network for… read more here.
Keywords: smooth functions; deep neural; neural networks; rectified power ... See more keywords