Results 41 to 50 of about 22,536 (196)
On \(\beta\)-differentiability of norms
Journal of Numerical Analysis and Approximation Theory, 2004 In this note we give some characterizations for the differentiability with respect to a bornology of a continuous convex function. The special case of seminorms is treated.
Valeriu Anisiu
doaj +2 more sources
Mathematics, 2022 In a real Hilbert space, we aim to investigate two modified Mann subgradient-like methods to find a solution to pseudo-monotone variational inequalities, which is also a common fixed point of a finite family of nonexpansive mappings and an asymptotically
Lu-Chuan Ceng +2 more
doaj +1 more source
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
Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks
, 2018 Motivated by machine learning applications in networks of sensors, internet-of-things (IoT) devices, and autonomous agents, we propose techniques for distributed stochastic convex learning from high-rate data streams.
Bajwa, Waheed U., Nokleby, Matthew
core +1 more source
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 +2 more
wiley +1 more source
On Stochastic Subgradient Mirror-Descent Algorithm with Weighted Averaging [PDF]
, 2013 This paper considers stochastic subgradient mirror-descent method for solving constrained convex minimization problems. In particular, a stochastic subgradient mirror-descent method with weighted iterate-averaging is investigated and its per-iterate ...
Angelia Nedic ́ +2 more
core
Heterogeneous Distributed Subgradient
, 2023 The paper proposes a heterogeneous push-sum based subgradient algorithm for multi-agent distributed convex optimization in which each agent can arbitrarily switch between subgradient-push and push-subgradient at each time. It is shown that the heterogeneous algorithm converges to an optimal point at an optimal rate over time-varying directed graphs.
Lin, Yixuan, Liu, Ji
openaire +2 more sources
Scaling Techniques for $\epsilon$-Subgradient Methods [PDF]
SIAM Journal on Optimization, 2016 Summary: The recent literature on first order methods for smooth optimization shows that significant improvements on the practical convergence behavior can be achieved with variable step size and scaling for the gradient, making this class of algorithms attractive for a variety of relevant applications.
BONETTINI, Silvia +2 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
One-Rank Linear Transformations and Fejer-Type Methods: An Overview
MathematicsSubgradient methods are frequently used for optimization problems. However, subgradient techniques are characterized by slow convergence for minimizing ravine convex functions.
Volodymyr Semenov +3 more
doaj +1 more source