Univariate \(L^p\) and \(l^p\) averaging, \(0

DOI10.3390/a5040421zbMath1461.60004OpenAlexW2119998971MaRDI QIDQ1736520

John E. Lavery

Publication date: 26 March 2019

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a5040421

zbMATH Keywords

medianheavy-tailed distributionpolynomial timeaveragestatistical structuremodeunivariate\(l^p\) average\(L^p\) averageradial monotonicity

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Computational methods for problems pertaining to probability theory (60-08) Probability distributions: general theory (60E05)

Cites Work

• Unnamed Item
• Unnamed Item
• Fast \(L_1^kC^k\) polynomial spline interpolation algorithm with shape-preserving properties
• A note on the complexity of \(L _{p }\) minimization
• Highly sparse representations from dictionaries are unique and independent of the sparseness measure
• Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
• Sparse approximations in signal and image processing
• Relaxing the uniformity and independence assumptions using the concept of fractal dimension
• Univariate cubic \(L_1\) interpolating splines: spline functional, window size and analysis-based algorithm
• Statistical data analysis based on the \(L_1\)-norm and related methods. With the technical assistance of Giuseppe Melfi. Papers of the 4th international conference on statistical analysis on the \(L_1\)-norm and related methods, Neuchâtel, Switzerland, August 4--9, 2002
• An Unconstrained $\ell_q$ Minimization with $0q\leq1$ for Sparse Solution of Underdetermined Linear Systems
• Robust principal component analysis?
• The Double Pareto-Lognormal Distribution—A New Parametric Model for Size Distributions
• On the Performance of Sparse Recovery Via $\ell_p$-Minimization $(0 \leq p \leq 1)$
• Heavy-Tail Phenomena

This page was built for publication: Univariate \(L^p\) and \(l^p\) averaging, \(0<p<1\), in polynomial time by utilization of statistical structure