Paper Citation Record · LEDGER

Optimized methods for composite optimization: a reduction perspective

As of 19 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 2 inbound Pith citation observations for arXiv:2506.23756.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.23756 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:42:48.422266Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:25:19.685884Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-25T05:46:39.650291Z

Reference resolution

60 of 60 outbound references displayed

verified exact2
verified fuzzy36
unresolved22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b77c7635-fa32-4018-94a5-68d38dc21bcd · outbound

This paper cites Altschuler.

Optimized methods for composite optimization: a reduction perspective Altschuler

Reference 1

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Observation c119deae-ea8c-4302-addf-2d2b28d251e6 · outbound

This paper cites Altschuler and Pablo A.

Optimized methods for composite optimization: a reduction perspective Altschuler and Pablo A

Reference 2

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Observation 87b2b12c-7eb9-43ac-98a2-bee839bcd2a6 · outbound

This paper cites Altschuler and Pablo A.

Optimized methods for composite optimization: a reduction perspective Altschuler and Pablo A

Reference 3

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Observation 885a3b2c-23d5-4899-a07c-8ac7471fa778 · outbound

This paper cites Gradient descent with non-convex constraints: local concavity determines convergence.

Optimized methods for composite optimization: a reduction perspective Gradient descent with non-convex constraints: local concavity determines convergence

Reference 4

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Observation 864a8da7-6951-4986-84f8-2a4c5d73fb8a · outbound

This paper cites Complexity guarantees for Polyak steps with momentum.

Optimized methods for composite optimization: a reduction perspective Complexity guarantees for Polyak steps with momentum

Reference 5

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Observation ea80ff7b-0ea6-4990-918a-11e42f4e0251 · outbound

This paper cites First-order methods in optimization , volume 25 of MOS-SIAM Series on Optimization.

Optimized methods for composite optimization: a reduction perspective First-order methods in optimization , volume 25 of MOS-SIAM Series on Optimization

Reference 6

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Observation 56096418-803f-4028-bfad-9cebaa82b10b · outbound

This paper cites Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems.

Optimized methods for composite optimization: a reduction perspective Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems

Reference 7

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Observation 6c268027-ab84-435e-aa52-3051634f8f8c · outbound

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

Optimized methods for composite optimization: a reduction perspective A fast iterative shrinkage-thresholding algorithm for linear inverse problems

Reference 8

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Observation 625a377f-86a5-41ea-b0dd-e7e1eefcc10a · outbound

This paper cites Accelerating Proximal Gradient Descent via Silver Stepsizes.

Optimized methods for composite optimization: a reduction perspective Accelerating Proximal Gradient Descent via Silver Stepsizes

Reference 9

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Observation c7072d16-3c7b-4e5f-bed4-5bda377af4fb · outbound

This paper cites Hendrickx, and Fran¸ cois Glineur.

Optimized methods for composite optimization: a reduction perspective Hendrickx, and Fran¸ cois Glineur

Reference 10

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Observation 2c0db447-504f-4db3-a124-5d5826e4080a · outbound

This paper cites Convex optimization.

Optimized methods for composite optimization: a reduction perspective Convex optimization

Reference 11

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Observation 6a0fe383-962d-41c1-a25d-02bbd577d1d6 · outbound

This paper cites Convex until proven guilty.

Optimized methods for composite optimization: a reduction perspective Convex until proven guilty

Reference 12

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Observation 72b6d89b-d7d9-4725-8153-33fe420838ce · outbound

This paper cites Performance estimation of the gradient method with fixed arbitrary step sizes.

Optimized methods for composite optimization: a reduction perspective Performance estimation of the gradient method with fixed arbitrary step sizes

Reference 13

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Observation 71c3aa7a-ba6c-4b4f-a2ee-00a5d23543e2 · outbound

This paper cites An iterative thresholding algorithm for linear inverse problems with a sparsity constraint.

Optimized methods for composite optimization: a reduction perspective An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

Reference 14

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Observation 3629a130-30ca-4298-9199-210d83d39459 · outbound

This paper cites The exact information-based complexity of smooth convex minimization.

Optimized methods for composite optimization: a reduction perspective The exact information-based complexity of smooth convex minimization

Reference 15

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Observation e87699b9-506d-4d8d-a53d-01fc56a73e20 · outbound

This paper cites Performance of first-order methods for smooth convex minimization: a novel approach.

Optimized methods for composite optimization: a reduction perspective Performance of first-order methods for smooth convex minimization: a novel approach

Reference 16

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Observation ebaa6ba0-c3f2-4a34-a89f-04e8f40b6d12 · outbound

This paper cites An optimal variant of Kelley’s cutting-plane method.

Optimized methods for composite optimization: a reduction perspective An optimal variant of Kelley’s cutting-plane method

Reference 17

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Observation b823abd2-cfa3-4fac-8932-71ea4f6d52e3 · outbound

This paper cites Acceleration methods.

Optimized methods for composite optimization: a reduction perspective Acceleration methods

Reference 18

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Observation 9efae749-38aa-4d93-996b-b1faac095525 · outbound

This paper cites Worst-case functions for the gradient method with fixed variable step sizes.

Optimized methods for composite optimization: a reduction perspective Worst-case functions for the gradient method with fixed variable step sizes

Reference 19

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Observation 900b19bc-3ea6-4693-8941-ab9d58e7d8f5 · outbound

This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

Optimized methods for composite optimization: a reduction perspective Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 20

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Observation c93a77f3-9e81-4dab-a4e5-855b75fe536e · outbound

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

Optimized methods for composite optimization: a reduction perspective Accelerated gradient methods for nonconvex nonlinear and stochastic programming

Reference 21

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Observation 71d37c4d-db4b-447c-b513-3cbe9af16775 · outbound

This paper cites Optimal first-order methods for convex functions with a quadratic upper bound.

Optimized methods for composite optimization: a reduction perspective Optimal first-order methods for convex functions with a quadratic upper bound

Reference 22

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Observation 3ecf411c-fd1e-4a3a-8a28-b035b22f6aaa · outbound

This paper cites Provably faster gradient descent via long steps.

Optimized methods for composite optimization: a reduction perspective Provably faster gradient descent via long steps

Reference 23

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

Reference 24

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

Reference 25

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Observation 8d7c192f-3c24-4d1b-baf0-56fe404b87e4 · outbound

This paper cites Tight Convergence Rate in Subgradient Norm of the Proximal Point Algorithm.

Optimized methods for composite optimization: a reduction perspective Tight Convergence Rate in Subgradient Norm of the Proximal Point Algorithm

Reference 26

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

Reference 27

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Observation 8ba5a14f-9aae-404d-ae03-3e1797e4eed4 · outbound

This paper cites Linear convergence of gradient and proximal-gradient methods under the Polyak- Lojasiewicz condition.

Optimized methods for composite optimization: a reduction perspective Linear convergence of gradient and proximal-gradient methods under the Polyak- Lojasiewicz condition

Reference 28

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Observation e055ad94-43ba-45b8-b931-baf51f2536fe · outbound

This paper cites Wainwright.

Optimized methods for composite optimization: a reduction perspective Wainwright

Reference 29

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This paper cites Accelerated proximal point method for maximally monotone operators.

Optimized methods for composite optimization: a reduction perspective Accelerated proximal point method for maximally monotone operators

Reference 30

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

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This paper cites Mirror Duality in Convex Optimization.

Optimized methods for composite optimization: a reduction perspective Mirror Duality in Convex Optimization

Reference 35

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This paper cites A Proof of the Exact Convergence Rate of Gradient Descent.

Optimized methods for composite optimization: a reduction perspective A Proof of the Exact Convergence Rate of Gradient Descent

Reference 36

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This paper cites A geometric structure of acceleration and its role in making gradients small fast.

Optimized methods for composite optimization: a reduction perspective A geometric structure of acceleration and its role in making gradients small fast

Reference 37

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

Reference 38

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This paper cites Lectures on convex optimization, volume 137 of Springer Optimization and Its Applica- tions.

Optimized methods for composite optimization: a reduction perspective Lectures on convex optimization, volume 137 of Springer Optimization and Its Applica- tions

Reference 39

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Reference 40

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This paper cites Exact worst-case convergence rates of gradi- ent descent: a complete analysis for all constant stepsizes over nonconvex and convex functions.

Optimized methods for composite optimization: a reduction perspective Exact worst-case convergence rates of gradi- ent descent: a complete analysis for all constant stepsizes over nonconvex and convex functions

Reference 42

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Optimized methods for composite optimization: a reduction perspective Ryu, Adrien B

Reference 43

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Optimized methods for composite optimization: a reduction perspective Cand` es

Reference 44

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This paper cites Towards principled and systematic approaches to the analysis and design of optimization algorithms.

Optimized methods for composite optimization: a reduction perspective Towards principled and systematic approaches to the analysis and design of optimization algorithms

Reference 45

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This paper cites An optimal gradient method for smooth strongly convex minimization.

Optimized methods for composite optimization: a reduction perspective An optimal gradient method for smooth strongly convex minimization

Reference 46

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This paper cites Taylor, Julien M.

Optimized methods for composite optimization: a reduction perspective Taylor, Julien M

Reference 47

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Observation 41672592-86d1-4b0e-aa60-210f07a5e0ad · outbound

This paper cites Taylor, Julien M.

Optimized methods for composite optimization: a reduction perspective Taylor, Julien M

Reference 48

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Observation 5164bf74-0a5b-498a-a7b5-296923c4e391 · outbound

This paper cites Taylor, Julien M.

Optimized methods for composite optimization: a reduction perspective Taylor, Julien M

Reference 49

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This paper cites An elementary approach to tight worst case complexity analysis of gradient based methods.

Optimized methods for composite optimization: a reduction perspective An elementary approach to tight worst case complexity analysis of gradient based methods

Reference 50

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Observation 10e15512-0dac-4f1a-9a71-603e3cb55c5f · outbound

This paper cites Relaxed Proximal Point Algorithm: Tight Complexity Bounds and Acceleration without Momentum.

Optimized methods for composite optimization: a reduction perspective Relaxed Proximal Point Algorithm: Tight Complexity Bounds and Acceleration without Momentum

Reference 51

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This paper cites Wilson, Ben Recht, and Michael I.

Optimized methods for composite optimization: a reduction perspective Wilson, Ben Recht, and Michael I

Reference 52

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

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Optimized methods for composite optimization: a reduction perspective Unresolved cited work

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Observation 0005a0ac-8683-4880-b62b-4cda51f44166 · outbound

This paper cites On Richardson’s method for solving linear systems with positive definite matrices.

Optimized methods for composite optimization: a reduction perspective On Richardson’s method for solving linear systems with positive definite matrices

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:49.569384Z

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Observation 6fd719b8-6c83-403e-b72a-92a813227ede · outbound

This paper cites The exact worst-case convergence rate of the alternating direction method of multipliers.

Optimized methods for composite optimization: a reduction perspective The exact worst-case convergence rate of the alternating direction method of multipliers

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:49.414602Z

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Observation 24a51a21-9221-4c02-addb-0ecc48c4b659 · outbound

This paper cites Exact convergence rate of the last iterate in subgradient methods.

Optimized methods for composite optimization: a reduction perspective Exact convergence rate of the last iterate in subgradient methods

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:48.196514Z

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Observation 94000ddd-c7e1-4b30-9498-e88f6227365d · outbound

This paper cites Accelerated gradient descent by concatenation of stepsize schedules.

Optimized methods for composite optimization: a reduction perspective Accelerated gradient descent by concatenation of stepsize schedules

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:48.272002Z

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Observation 6f8b47d5-1abb-4fc0-aed4-61944243ff42 · outbound

This paper cites Anytime Acceleration of Gradient Descent.

Optimized methods for composite optimization: a reduction perspective Anytime Acceleration of Gradient Descent

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:48.332185Z

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Observation 37b6c6bd-9034-4999-862d-ceade1fdeb34 · outbound

This paper cites an unresolved cited work.

Optimized methods for composite optimization: a reduction perspective Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:42:49.286486Z

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Pith citing papers

Observation 90d69565-72d2-494d-8904-e5038aefbda2 · inbound

On the convergence rate of the Douglas-Rachford splitting algorithm cites this paper.

On the convergence rate of the Douglas-Rachford splitting algorithm Optimized methods for composite optimization: a reduction perspective

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T23:25:19.685884Z

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Observation ea425a05-a6b3-477b-b673-21ba95424366 · inbound

An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit cites this paper.

An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit Optimized methods for composite optimization: a reduction perspective

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:46:39.653191Z

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