Results 41 to 50 of about 884 (184)

Adaptive Weighted Total Variation Penalty for Precise Change Point Detection

Australian &New Zealand Journal of Statistics, Volume 68, Issue 2, June 2026.
ABSTRACT Total variation (TV)‐based methods, such as the fused lasso, are standard for change point detection but are impaired by issues like local monotonicity. To address these limitations, this study comparatively analyses the fused lasso with two alternative methodologies.
Dong‐Young Lee, Kwan‐Young Bak, Jae‐Hwan Jhong   +2 more
wiley   +1 more source

A trust region method using subgradient for minimizing a nondifferentiable function [PDF]

Yugoslav Journal of Operations Research, 2009
The minimization of a particular nondifferentiable function is considered. The first and second order necessary conditions are given. A trust region method for minimization of this form of the objective function is presented.
Gardašević-Filipović Milanka
doaj   +1 more source

On Bounds for Norms of Reparameterized ReLU Artificial Neural Network Parameters: Sums of Fractional Powers of the Lipschitz Norm Control the Network Parameter Vector

Mathematical Methods in the Applied Sciences, Volume 49, Issue 4, Page 2135-2160, 15 March 2026.
ABSTRACT It is an elementary fact in the scientific literature that the Lipschitz norm of the realization function of a feedforward fully connected rectified linear unit (ReLU) artificial neural network (ANN) can, up to a multiplicative constant, be bounded from above by sums of powers of the norm of the ANN parameter vector.
Arnulf Jentzen, Timo Kröger
wiley   +1 more source

One-Rank Linear Transformations and Fejer-Type Methods: An Overview

Mathematics
Subgradient methods are frequently used for optimization problems. However, subgradient techniques are characterized by slow convergence for minimizing ravine convex functions.
Volodymyr Semenov, Petro Stetsyuk, Viktor Stovba, José Manuel Velarde Cantú   +3 more
doaj   +1 more source

A Proposal of Smooth Interpolation to Optimal Transport for Restoring Biased Data for Algorithmic Fairness

Applied Stochastic Models in Business and Industry, Volume 42, Issue 2, March/April 2026.
ABSTRACT The so‐called algorithmic bias is a hot topic in the decision‐making process based on Artificial Intelligence, especially when demographics, such as gender, age or ethnic origin, come into play. Frequently, the problem is not only in the algorithm itself, but also in the biased data that feed the algorithm, which is just the reflection of the ...
Elena M. De‐Diego, Paula Gordaliza, Jesús López‐Fidalgo   +2 more
wiley   +1 more source

Subgradient Algorithm, Stochastic Subgradient Algorithm, Incremental Subgradient Algorithm, and Set Location Problems

, 2013
The results in this manuscript has been incorporated to another ...
Nam, Nguyen Mau, An, Nguyen Thai, Le, Han   +2 more
openaire   +2 more sources

Distributed subgradient methods and quantization effects [PDF]

2008 47th IEEE Conference on Decision and Control, 2008
We consider a convex unconstrained optimization problem that arises in a network of agents whose goal is to cooperatively optimize the sum of the individual agent objective functions through local computations and communications. For this problem, we use averaging algorithms to develop distributed subgradient methods that can operate over a time ...
Angelia Nedic, Alexander Olshevsky, Asuman E. Ozdaglar, John N. Tsitsiklis   +3 more
openaire   +2 more sources

T‐calibration in semi‐parametric models

Canadian Journal of Statistics, Volume 54, Issue 1, March 2026.
AbstractThis article relates the calibration of models to the consistent loss functions for the target functional of the model. Correctly specified models are calibrated. Conversely, we demonstrate that if there is a parameter value that is optimal under all consistent loss functions, then a model is calibrated.
Anja Mühlemann, Johanna Ziegel
wiley   +1 more source

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

IEEE Access, 2023
We focus on a distributed learning problem in a communication network, consisting of $N$ distributed nodes and a central parameter server (PS). The PS is responsible for performing the computation based on data received from the nodes, which are ...
Tamir L. S. Gez, Kobi Cohen
doaj   +1 more source

Estimating Plant Species Richness With Sentinel and Landsat Data Across Ecosystems in China

Ecology and Evolution, Volume 16, Issue 3, March 2026.
438 field plots were used to estimate plant diversity across ecosystems; 18 spectral indices were derived from Sentinel and Landsat data; EVI, DVI, PSRI, NDVI, PRI, GNDVI, and GMEVI were identified as powerful indicators for predicting plant alpha diversity.
Keman Wang, Yu Peng, Ying Ye, Yue Qi, Lizheng He   +4 more
wiley   +1 more source