Paper Citation Record · LEDGER

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems

As of 13 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2411.16743.

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pith.paper-citation-record.v1
2411.16743 v1

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measured 33 of 33 reference resolution

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33 of 33 outbound references displayed

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Outbound references

Observation 80fdcbc5-7d3a-4e0c-9a79-61bc46892cc9 · outbound

This paper cites Lectures on convex optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Lectures on convex optimization

Reference 1

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Observation 50af7b2e-10b3-4b69-809f-139fc20de074 · outbound

This paper cites First-Order Methods in Optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems First-Order Methods in Optimization

Reference 2

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This paper cites relative continuity for non-lipschitz nonsmooth convex optimization using stochastic (or deterministic) mirror descent.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems relative continuity for non-lipschitz nonsmooth convex optimization using stochastic (or deterministic) mirror descent

Reference 3

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This paper cites A descent Lemma beyond Lipschitz gradient continuity: first-order method revisited and applications.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems A descent Lemma beyond Lipschitz gradient continuity: first-order method revisited and applications

Reference 4

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This paper cites Relatively smooth convex optimization by first-order methods, and applications.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Relatively smooth convex optimization by first-order methods, and applications

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Observation 680f357c-be2c-4592-ab44-485a51fc94c0 · outbound

This paper cites Accelerated Bregman proximal gradient methods for relatively smooth convex optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Accelerated Bregman proximal gradient methods for relatively smooth convex optimization

Reference 6

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Observation c6fb2efb-58f7-4619-83fd-828385eb55a4 · outbound

This paper cites Implementable tensor methods in unconstrained convex optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Implementable tensor methods in unconstrained convex optimization

Reference 7

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Observation 4440ee2b-35bb-4f05-b415-44fcbe06e50e · outbound

This paper cites Inexact accelerated high-order proximal-point methods.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Inexact accelerated high-order proximal-point methods

Reference 8

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Observation 5508a24d-0536-4ae6-aee6-563913a09d36 · outbound

This paper cites Optimization methods for large-scale machine learning.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Optimization methods for large-scale machine learning

Reference 9

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This paper cites Random search for hyper-parameter optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Random search for hyper-parameter optimization

Reference 10

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Observation d57253b0-7d51-46fd-a293-e1347cbf4c6f · outbound

This paper cites First-order methods of smooth convex optimization with inexact oracle.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems First-order methods of smooth convex optimization with inexact oracle

Reference 11

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Observation 47f4f8d4-6ad2-4b2d-8e2d-4b96b8180618 · outbound

This paper cites Inexact model: A framework for optimiza- tion and variational inequalities.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Inexact model: A framework for optimiza- tion and variational inequalities

Reference 12

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Observation 2eb2ddd2-aa39-40da-9418-4eecea4cb3d2 · outbound

This paper cites Universal intermediate gradient method for convex problems with inexact oracle.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Universal intermediate gradient method for convex problems with inexact oracle

Reference 13

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This paper cites Intermediate gradient methods for smooth convex problems with inexact oracle.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Intermediate gradient methods for smooth convex problems with inexact oracle

Reference 14

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Observation 166c903f-4160-4247-a05b-f819debb4869 · outbound

This paper cites Stochastic intermediate gradient method for convex prob- lems with stochastic inexact oracle.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Stochastic intermediate gradient method for convex prob- lems with stochastic inexact oracle

Reference 15

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Observation a95d649a-3729-49ee-b474-e1e8753f82ee · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems A fast iterative shrinkage-thresholding algorithm for linear inverse problems

Reference 16

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This paper cites Gradient methods for minimizing composite functions.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Gradient methods for minimizing composite functions

Reference 17

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Observation 8cc954a1-00d7-4aec-8d06-09f4ff106cf6 · outbound

This paper cites Interior gradient and proximal methods for convex and conic op- timization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Interior gradient and proximal methods for convex and conic op- timization

Reference 18

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Observation 2bdb9706-b46a-43ef-8ecc-c56d5f0523dc · outbound

This paper cites Problem Complexity and Method Efficiency in Optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Problem Complexity and Method Efficiency in Optimization

Reference 19

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Observation be0c606a-47e7-4c32-83b3-d61b0fcfb243 · outbound

This paper cites Image deblurring with Poisson data: from cells to galaxies.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Image deblurring with Poisson data: from cells to galaxies

Reference 20

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This paper cites Why least squares and maximum entropy an axiomatic approach to inference for linear iverse problems.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Why least squares and maximum entropy an axiomatic approach to inference for linear iverse problems

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Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Optimum design in regression problems

Reference 22

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Observation 7531ba7e-eb1a-42a3-bcae-f4373de8c959 · outbound

This paper cites Optimal and efficient designs of experiments.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Optimal and efficient designs of experiments

Reference 23

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This paper cites A simple convergence analysis of Bregman proximal gradient algorithm.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems A simple convergence analysis of Bregman proximal gradient algorithm

Reference 24

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Observation a74ffa0a-ef7a-4608-abcc-b6b45879065d · outbound

This paper cites Convergence analysis of a proximal-like minimization algorithm us- ing Bregman functions.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Convergence analysis of a proximal-like minimization algorithm us- ing Bregman functions

Reference 25

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This paper cites Learning supervised pagerank with gradient-based and gradient-free optimization methods.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Learning supervised pagerank with gradient-based and gradient-free optimization methods

Reference 26

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Observation f1eaeb10-08bf-438b-8c53-a93e5e1b2ea3 · outbound

This paper cites Universal gradient methods for convex optimization problems.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Universal gradient methods for convex optimization problems

Reference 27

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This paper cites Adaptive Algorithms for Relatively Lips- chitz Continuous Convex Optimization Problems.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Adaptive Algorithms for Relatively Lips- chitz Continuous Convex Optimization Problems

Reference 28

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This paper cites Adaptive Methods or Variational Inequalities with Relatively Smooth and Reletively Strongly Monotone Operators.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Adaptive Methods or Variational Inequalities with Relatively Smooth and Reletively Strongly Monotone Operators

Reference 29

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Observation e038a90d-c0ff-444e-b3d0-31c4f5e9759b · outbound

This paper cites Lectures on modern convex optimization: analysis, algorithms, and engineering applications.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Lectures on modern convex optimization: analysis, algorithms, and engineering applications

Reference 30

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Observation 7ffe5bef-e43e-4c82-b99a-f72496208212 · outbound

This paper cites A simplified view of first order methods for optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems A simplified view of first order methods for optimization

Reference 31

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Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Pegasos: primal estimated sub-gradient solver for SVM

Reference 32

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This paper cites Bregman gradient methods for relatively-smooth optimization.

Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems Bregman gradient methods for relatively-smooth optimization

Reference 33

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