On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation | Paper Citation Record

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This paper cites Lower bounds for non-convex stochastic optimization.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Lower bounds for non-convex stochastic optimization

Reference 1

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This paper cites Fixed-point algorithms for inverse problems in science and engineering , volume 49.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Fixed-point algorithms for inverse problems in science and engineering , volume 49

Reference 2

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

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation A fast iterative shrinkage-thresholding algorithm for linear inverse problems

Reference 3

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Adaptive sampling strategies for risk-averse stochastic optimization with constraints

Reference 4

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This paper cites Convex optimization algorithms.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Convex optimization algorithms

Reference 5

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This paper cites Adaptive sampling strategies for stochastic optimization.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Adaptive sampling strategies for stochastic optimization

Reference 6

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This paper cites Inertial variable metric tech- niques for the inexact forward–backward algorithm.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Inertial variable metric tech- niques for the inexact forward–backward algorithm

Reference 7

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This paper cites Sample size selection in optimization methods for machine learning.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Sample size selection in optimization methods for machine learning

Reference 8

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This paper cites Lower bounds for finding stationary points i.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Lower bounds for finding stationary points i

Reference 9

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This paper cites Lower bounds for finding stationary points ii: first-order methods.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Lower bounds for finding stationary points ii: first-order methods

Reference 10

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This paper cites On the global convergence of trust region algorithms using inexact gradient information.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation On the global convergence of trust region algorithms using inexact gradient information

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This paper cites LIBSVM: A library for support vector machines.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation LIBSVM: A library for support vector machines

Reference 12

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This paper cites Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso

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This paper cites Composite objective mirror descent.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Composite objective mirror descent

Reference 14

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This paper cites Total variation projection with first order schemes.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Total variation projection with first order schemes

Reference 15

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This paper cites Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework

Reference 16

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This paper cites Accelerated gradient methods for nonconvex nonlinear and stochastic programming.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Accelerated gradient methods for nonconvex nonlinear and stochastic programming

Reference 17

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This paper cites Deblurring images: matrices, spectra, and filtering.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Deblurring images: matrices, spectra, and filtering

Reference 18

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This paper cites Accelerated gradient methods for stochastic optimization and online learning.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Accelerated gradient methods for stochastic optimization and online learning

Reference 19

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization

Reference 20

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This paper cites Smoothed variable sample-size accelerated proximal methods for nonsmooth stochastic convex programs.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Smoothed variable sample-size accelerated proximal methods for nonsmooth stochastic convex programs

Reference 21

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This paper cites Proximal meth- ods for sparse hierarchical dictionary learning.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Proximal meth- ods for sparse hierarchical dictionary learning

Reference 22

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This paper cites A generic acceleration framework for stochastic com- posite optimization.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation A generic acceleration framework for stochastic com- posite optimization

Reference 23

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This paper cites An optimal method for stochastic composite optimization.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation An optimal method for stochastic composite optimization

Reference 24

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This paper cites On the Convergence of FedAvg on Non-IID Data.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation On the Convergence of FedAvg on Non-IID Data

Reference 25

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation A simple proximal stochastic gradient method for nonsmooth nonconvex optimization

Reference 26

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Zeroth-order gradient and quasi-newton methods for nonsmooth nonconvex stochastic optimization

Reference 27

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Network newton distributed optimization methods

Reference 28

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Gradient methods for minimizing composite functions

Reference 29

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation A method for unconstrained convex minimization problem with the rate of convergence o (1/k2)

Reference 30

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This paper cites Lectures on convex optimization , volume 137.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Lectures on convex optimization , volume 137

Reference 31

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization

Reference 32

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods

Reference 33

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Proxsarah: An efficient algorithmic framework for stochastic composite nonconvex optimization

Reference 34

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Scaled, inexact, and adaptive generalized fista for strongly convex optimization

Reference 35

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This paper cites Fast first-order methods for composite con- vex optimization with backtracking.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Fast first-order methods for composite con- vex optimization with backtracking

Reference 36

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This paper cites Convergence rates of inexact proximal- gradient methods for convex optimization.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Convergence rates of inexact proximal- gradient methods for convex optimization

Reference 37

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Practical bayesian optimization of machine learning algorithms

Reference 38

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Convergence rates of accelerated proximal gradient algorithms under independent noise

Reference 39

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Conditional convergence of infinite products

Reference 40

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This paper cites Computational methods for sparse solution of linear inverse problems.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Computational methods for sparse solution of linear inverse problems

Reference 41

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Unresolved cited work

Reference 42

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This paper cites Constrained and com- posite optimization via adaptive sampling methods.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Constrained and com- posite optimization via adaptive sampling methods

Reference 43

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This paper cites Private Zeroth-Order Nonsmooth Nonconvex Optimization.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Private Zeroth-Order Nonsmooth Nonconvex Optimization

Reference 44

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On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Unresolved cited work

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This paper cites Determin- istic.

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation Determin- istic

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