Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under $(L_0,L_1)$-Smoothness
Fuente:
arXiv
Saved in:
| Main Authors: | Lobanov, Aleksandr, Gasnikov, Alexander, Gorbunov, Eduard, Takáč, Martin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
by: Lobanov, Aleksandr, et al.
Published: (2025)
by: Lobanov, Aleksandr, et al.
Published: (2025)
Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
by: Chezhegov, Savelii, et al.
Published: (2025)
by: Chezhegov, Savelii, et al.
Published: (2025)
Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
by: Gorbunov, Eduard, et al.
Published: (2024)
by: Gorbunov, Eduard, et al.
Published: (2024)
Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem under "Overparametrization" Condition
by: Lobanov, Aleksandr, et al.
Published: (2023)
by: Lobanov, Aleksandr, et al.
Published: (2023)
Median Clipping for Zeroth-order Non-Smooth Convex Optimization and Multi-Armed Bandit Problem with Heavy-tailed Symmetric Noise
by: Kornilov, Nikita, et al.
Published: (2024)
by: Kornilov, Nikita, et al.
Published: (2024)
Randomized gradient-free methods in convex optimization
by: Gasnikov, Alexander, et al.
Published: (2022)
by: Gasnikov, Alexander, et al.
Published: (2022)
The Black-Box Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation
by: Lobanov, Aleksandr, et al.
Published: (2023)
by: Lobanov, Aleksandr, et al.
Published: (2023)
Gradient-free algorithm for saddle point problems under overparametrization
by: Statkevich, Ekaterina, et al.
Published: (2024)
by: Statkevich, Ekaterina, et al.
Published: (2024)
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
by: Demidovich, Yury, et al.
Published: (2024)
by: Demidovich, Yury, et al.
Published: (2024)
Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
by: Lobanov, Aleksandr, et al.
Published: (2024)
by: Lobanov, Aleksandr, et al.
Published: (2024)
Improved Iteration Complexity in Black-Box Optimization Problems under Higher Order Smoothness Function Condition
by: Lobanov, Aleksandr
Published: (2024)
by: Lobanov, Aleksandr
Published: (2024)
Last Iterate Convergence of AdaGrad-Norm for Convex Non-Smooth Optimization
by: Preobrazhenskaia, Margarita, et al.
Published: (2026)
by: Preobrazhenskaia, Margarita, et al.
Published: (2026)
Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and $(L_0, L_1)$-Smoothness
by: Tyurin, Alexander
Published: (2025)
by: Tyurin, Alexander
Published: (2025)
OPTAMI: Global Superlinear Convergence of High-order Methods
by: Kamzolov, Dmitry, et al.
Published: (2024)
by: Kamzolov, Dmitry, et al.
Published: (2024)
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
by: Agafonov, Artem, et al.
Published: (2024)
by: Agafonov, Artem, et al.
Published: (2024)
Avoiding Bias in Clipped SGD for Overparameterized Models under Generalized Smoothness
by: Lobanov, Aleksandr, et al.
Published: (2026)
by: Lobanov, Aleksandr, et al.
Published: (2026)
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
by: Chezhegov, Savelii, et al.
Published: (2024)
by: Chezhegov, Savelii, et al.
Published: (2024)
Adaptive Regularized Newton Method with Inexact Hessian
by: Shestakov, Aleksandr, et al.
Published: (2025)
by: Shestakov, Aleksandr, et al.
Published: (2025)
Nesterov's method of dichotomy via Order Oracle: The problem of optimizing a two-variable function on a square
by: Chervonenkis, Boris, et al.
Published: (2024)
by: Chervonenkis, Boris, et al.
Published: (2024)
High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2021)
by: Gorbunov, Eduard, et al.
Published: (2021)
Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks
by: Divilkovskiy, Maxim, et al.
Published: (2025)
by: Divilkovskiy, Maxim, et al.
Published: (2025)
Convergence of Clipped SGD on Convex $(L_0,L_1)$-Smooth Functions
by: Gaash, Ofir, et al.
Published: (2025)
by: Gaash, Ofir, et al.
Published: (2025)
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
by: Kornilov, Nikita, et al.
Published: (2025)
by: Kornilov, Nikita, et al.
Published: (2025)
Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
by: Agafonov, Artem, et al.
Published: (2025)
by: Agafonov, Artem, et al.
Published: (2025)
Gradient Descent for Convex and Smooth Noisy Optimization
by: Hu, Feifei, et al.
Published: (2024)
by: Hu, Feifei, et al.
Published: (2024)
Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions
by: Sadchikov, Andrey, et al.
Published: (2024)
by: Sadchikov, Andrey, et al.
Published: (2024)
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods
by: Vankov, Daniil, et al.
Published: (2024)
by: Vankov, Daniil, et al.
Published: (2024)
Federated Learning Can Find Friends That Are Advantageous
by: Tupitsa, Nazarii, et al.
Published: (2024)
by: Tupitsa, Nazarii, et al.
Published: (2024)
Accelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks
by: Solodkin, Vladimir, et al.
Published: (2024)
by: Solodkin, Vladimir, et al.
Published: (2024)
Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent
by: Rotaru, Teodor, et al.
Published: (2025)
by: Rotaru, Teodor, et al.
Published: (2025)
About some works of Boris Polyak on convergence of gradient methods and their development
by: Ablaev, Seydamet, et al.
Published: (2023)
by: Ablaev, Seydamet, et al.
Published: (2023)
Toward a Unified Theory of Gradient Descent under Generalized Smoothness
by: Tyurin, Alexander
Published: (2024)
by: Tyurin, Alexander
Published: (2024)
Bregman Proximal Method for Efficient Communications under Similarity
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
On Convergence of Incremental Gradient for Non-Convex Smooth Functions
by: Koloskova, Anastasia, et al.
Published: (2023)
by: Koloskova, Anastasia, et al.
Published: (2023)
Adaptive Variant of Frank-Wolfe Method for Relative Smooth Convex Optimization Problems
by: Vyguzov, Alexander, et al.
Published: (2024)
by: Vyguzov, Alexander, et al.
Published: (2024)
Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
by: Veprikov, Andrey, et al.
Published: (2025)
by: Veprikov, Andrey, et al.
Published: (2025)
Newton Method Revisited: Global Convergence Rates up to $\mathcal {O}\left(k^{-3} \right)$ for Stepsize Schedules and Linesearch Procedures
by: Hanzely, Slavomír, et al.
Published: (2024)
by: Hanzely, Slavomír, et al.
Published: (2024)
A Proof of the Exact Convergence Rate of Gradient Descent
by: Kim, Jungbin
Published: (2024)
by: Kim, Jungbin
Published: (2024)
Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities
by: Pichugin, Alexander, et al.
Published: (2024)
by: Pichugin, Alexander, et al.
Published: (2024)
Similar Items
-
Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
by: Lobanov, Aleksandr, et al.
Published: (2025) -
Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
by: Chezhegov, Savelii, et al.
Published: (2025) -
Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
by: Gorbunov, Eduard, et al.
Published: (2024) -
Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem under "Overparametrization" Condition
by: Lobanov, Aleksandr, et al.
Published: (2023) -
Median Clipping for Zeroth-order Non-Smooth Convex Optimization and Multi-Armed Bandit Problem with Heavy-tailed Symmetric Noise
by: Kornilov, Nikita, et al.
Published: (2024)