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Paper Citation Record · LEDGER

Solving Zero-Sum Games with Fewer Matrix-Vector Products

As of 20 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2509.04426.

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pith.paper-citation-record.v1
2509.04426 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 52 of 52 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 8185e5f9-3ba1-4068-acdb-59c4a76516a1 · outbound

This paper cites Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities

Reference 1

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Observation 1cfec7b8-08fd-48a3-8b67-10d8f6ccbc6e · outbound

This paper cites Stochastic bias-reduced gradi- ent methods.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Stochastic bias-reduced gradi- ent methods

Reference 2

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Observation 1b288868-11d2-4f41-8842-3e7f48c48671 · outbound

This paper cites Bailey and Georgios Piliouras.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Bailey and Georgios Piliouras

Reference 3

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Observation d49617d7-e01e-4d2f-8db9-1d34b10d2b63 · outbound

This paper cites Low-rank approximation with matrix- vector products.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Low-rank approximation with matrix- vector products

Reference 4

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Observation e1c5bad0-8cb8-40d0-a170-cb5bc5528750 · outbound

This paper cites The gradient complexity of linear regression.

Solving Zero-Sum Games with Fewer Matrix-Vector Products The gradient complexity of linear regression

Reference 5

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Observation 271ddd10-cef2-4dc0-87e7-0dbaf0f8e8b9 · outbound

This paper cites Complexity of highly parallel non-smooth convex optimization.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Complexity of highly parallel non-smooth convex optimization

Reference 6

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Observation 88f12b61-4bed-4f29-be88-46042d934fdf · outbound

This paper cites Near-optimal method for highly smooth convex optimization.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Near-optimal method for highly smooth convex optimization

Reference 7

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Observation 4fb91cd6-f053-44ae-8abd-433853120ee4 · outbound

This paper cites Distributionally Robust Optimization via Ball Oracle Acceleration.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Distributionally Robust Optimization via Ball Oracle Acceleration

Reference 8

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Observation cdfd7706-34da-4c2c-bb50-68089ab23ae2 · outbound

This paper cites Convex until proven guilty: dimension- free acceleration of gradient descent on non-convex functions.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Convex until proven guilty: dimension- free acceleration of gradient descent on non-convex functions

Reference 9

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Observation 1498986f-d9ca-4600-ba25-5ab6eba43325 · outbound

This paper cites Variance reduction for matrix games.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Variance reduction for matrix games

Reference 10

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Observation 2b195ad0-c53e-4d64-9177-b5d227456c87 · outbound

This paper cites Acceleration with a ball optimization oracle.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Acceleration with a ball optimization oracle

Reference 11

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Source-reported events for the cited work

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Observation 616ca0bf-6c71-4498-ada4-28baf51e8677 · outbound

This paper cites Coordinate methods for matrix games.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Coordinate methods for matrix games

Reference 12

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Observation c09be62c-c4db-4758-ba04-1cc9f3d19849 · outbound

This paper cites Thinking inside the ball: Near-optimal minimization of the maximal loss.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Thinking inside the ball: Near-optimal minimization of the maximal loss

Reference 13

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This paper cites Optimal and adap- tive monteiro-svaiter acceleration.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Optimal and adap- tive monteiro-svaiter acceleration

Reference 14

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Observation 387b12bc-fafa-4cab-8032-e8e6aaa6b20c · outbound

This paper cites Resqueing parallel and private stochastic convex optimization.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Resqueing parallel and private stochastic convex optimization

Reference 15

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Observation debe3548-751e-481a-97e8-058ba90f7162 · outbound

This paper cites A whole new ball game: A primal accelerated method for matrix games and minimizing the maximum of smooth functions.

Solving Zero-Sum Games with Fewer Matrix-Vector Products A whole new ball game: A primal accelerated method for matrix games and minimizing the maximum of smooth functions

Reference 16

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Observation 67ba49dc-a726-48e8-83f4-127cf96a18f2 · outbound

This paper cites Sublinear optimization for machine learning.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Sublinear optimization for machine learning

Reference 17

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Observation 0cbf91b3-a462-4ee8-ac3c-9c217f05d09f · outbound

This paper cites Relative lipschitzness in extragradient methods and a direct recipe for acceleration.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Relative lipschitzness in extragradient methods and a direct recipe for acceleration

Reference 18

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Observation c051648e-bd92-4e9e-89ad-09a9a39b53c6 · outbound

This paper cites Near-optimal no-regret algorithms for zero-sum games.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Near-optimal no-regret algorithms for zero-sum games

Reference 19

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Observation 95d947ff-b67a-4d55-a507-e440033b089c · outbound

This paper cites Composite objective mirror descent.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Composite objective mirror descent

Reference 20

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Observation 1d58cd0f-73b7-4426-a619-9d1539e9e79d · outbound

This paper cites Adaptive game playing using multiplicative weights.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Adaptive game playing using multiplicative weights

Reference 21

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Solving Zero-Sum Games with Fewer Matrix-Vector Products Generative adversarial nets

Reference 22

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Source-reported events for the cited work

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Observation 9699e90f-b920-4855-adab-9900f91bfe1e · outbound

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Solving Zero-Sum Games with Fewer Matrix-Vector Products A sublinear-time randomized approximation algorithm for matrix games

Reference 23

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Solving Zero-Sum Games with Fewer Matrix-Vector Products On lower complexity bounds for large-scale smooth convex optimization

Reference 24

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This paper cites Towards characterizing the first-order query complexity of learning (approximate) nash equilibria in zero-sum matrix games.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Towards characterizing the first-order query complexity of learning (approximate) nash equilibria in zero-sum matrix games

Reference 25

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Observation 624861d4-73bb-4290-bf94-5ad0c15a5735 · outbound

This paper cites Mirror prox algorithm for multi-term composite minimization and semi-separable problems.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Mirror prox algorithm for multi-term composite minimization and semi-separable problems

Reference 26

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Observation 3a25ede7-eb2e-4eb2-94c8-66ceba0dbd48 · outbound

This paper cites Closing the computational-query depth gap in parallel stochastic convex optimization.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Closing the computational-query depth gap in parallel stochastic convex optimization

Reference 27

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Source-reported events for the cited work

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Observation 6cdca6a5-cad5-43e7-a776-0c2f64006cd6 · outbound

This paper cites Reusing Samples in Variance Reduction.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Reusing Samples in Variance Reduction

Reference 28

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Source-reported events for the cited work

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Observation f441c358-3f9a-4327-ae1a-63987d21374a · outbound

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Solving Zero-Sum Games with Fewer Matrix-Vector Products Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4fe60f72-74da-4179-9c0a-abea1d30002a · outbound

This paper cites The oracle complexity of simplex-based matrix games: Linear separability and nash equilibria.

Solving Zero-Sum Games with Fewer Matrix-Vector Products The oracle complexity of simplex-based matrix games: Linear separability and nash equilibria

Reference 30

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Observation 80bb1762-a2f9-4e4f-9047-24e4bf7acbbe · outbound

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Solving Zero-Sum Games with Fewer Matrix-Vector Products Unresolved cited work

Reference 31

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Observation a4782438-104c-4e6a-b0b5-b53af2ce611c · outbound

This paper cites The weighted majority algorithm.

Solving Zero-Sum Games with Fewer Matrix-Vector Products The weighted majority algorithm

Reference 32

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Source-reported events for the cited work

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Observation 60b089f6-30ec-4297-a927-19dc892648d4 · outbound

This paper cites Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization

Reference 33

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Observation f523ef2f-60f3-4c0f-8b13-e11a8d24050d · outbound

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Solving Zero-Sum Games with Fewer Matrix-Vector Products Towards deep learning models resistant to adversarial attacks

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5e8b9c8b-18eb-4829-a8c1-bef43a3af37b · outbound

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Solving Zero-Sum Games with Fewer Matrix-Vector Products A study of local approximations in information theory

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.086640Z digest=sha256:afbf8167b29b30c0c5ff30292c9727b35e12b5bd8457f2f45bbf1f332dc9751f

Observation d4f301de-9c0b-4c1b-9cb4-cc8f8cb6c25d · outbound

This paper cites A logical calculus of the ideas immanent in nervous activity.

Solving Zero-Sum Games with Fewer Matrix-Vector Products A logical calculus of the ideas immanent in nervous activity

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.490839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.091820Z digest=sha256:cbbd28c79d94d105b97de97fba156e62d890630c25b84523e9dd7a2e4e75cd08

Observation b7a84680-8a0e-40d0-ab7d-bd3d40e3bc10 · outbound

This paper cites an unresolved cited work.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-15T16:41:53.476833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.096123Z digest=sha256:267b1f8c5cb983843883a3ab0e29fa4e11c7d5b58aef1322cba61db3a600fcad

Observation 5c3dcf46-88e2-4719-b8cc-0ee2a4fb05a3 · outbound

This paper cites Randomized block krylov methods for stronger and faster approximate singular value decomposition.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Randomized block krylov methods for stronger and faster approximate singular value decomposition

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.463282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.100464Z digest=sha256:2de5dd8c6df2bc90c911294875b55f0b98d2aa39179efa5abddbd15c4213e0da

Observation cf77161f-2798-4628-b477-5f402ad93f54 · outbound

This paper cites Prox-method with rate of convergence o(1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Prox-method with rate of convergence o(1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-15T16:41:53.104932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:53.104932Z digest=sha256:65819285ecc501c87f6777d38d0fa55f330008e75b097149be9f7c9a7887d334

Observation 2cf37bb4-7ed4-48a2-8593-80bf9d8c061f · outbound

This paper cites Problem complexity and method efficiency in optimization.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Problem complexity and method efficiency in optimization

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.440891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.109434Z digest=sha256:b1602abf81780666663461015880dbf96894c128a3f6a4b2cf051420eca35cf1

Observation 932acd40-eafb-48fa-a5f1-f9bc850cd78f · outbound

This paper cites Smooth minimization of non-smooth functions.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Smooth minimization of non-smooth functions

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.427469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.114270Z digest=sha256:033c9f5cebfc1ce94cac12ba5e9ceae9b3a4b250f060a2345fd5d1da1b0322c2

Observation 229ef9a0-bd45-49a1-9def-fa7a3e590124 · outbound

This paper cites Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.412819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.118502Z digest=sha256:0e297fa08f3d2546ce904b54f19f95ae28f89b0c40705fab3c93642f7a2193a2

Observation fea293be-59f8-479d-9645-96571c6366fb · outbound

This paper cites Stochastic variance reduction methods for saddle-point problems.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Stochastic variance reduction methods for saddle-point problems

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.399163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.122651Z digest=sha256:a0f52df209761ddc31ca9022c040033382022cdd2740f969b0b287ca777bc49e

Observation 36df7897-f0da-4dd0-bf8f-4c1afc867b1c · outbound

This paper cites Optimization, learning, and games with predictable sequences.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Optimization, learning, and games with predictable sequences

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-15T16:41:53.127523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:53.127523Z digest=sha256:7c5a1b87cfacdec9a6c931e8b364cc45c9ac460f7f26cb0b7508b56927872d1a

Observation 218c16bf-dc8f-4267-969b-5e8e44bf122d · outbound

This paper cites Estimation of high-dimensional low-rank matrices.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Estimation of high-dimensional low-rank matrices

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.376419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.131197Z digest=sha256:09d41edd7e8b55fde06398a094caea4acb2b952d90a979d21ea39ac6ad80127c

Observation d934c9a9-35b7-4c86-88d1-7c2189410999 · outbound

This paper cites The perceptron: a probabilistic model for information storage and organization in the brain.

Solving Zero-Sum Games with Fewer Matrix-Vector Products The perceptron: a probabilistic model for information storage and organization in the brain

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.362309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.134921Z digest=sha256:99a52461150310c21c5984d8a673488975d20eea71b9ecdaa20d804bc5e66d3a

Observation d1354fa1-59dd-4cdc-948e-b822f8ec5b7e · outbound

This paper cites Online learning and online convex optimization.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Online learning and online convex optimization

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.346938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.138672Z digest=sha256:b1aaeff9a1bb188fb62f66c75ca8f37fdd21c4befd1fd70e97f2eef8f1f1d11c

Observation 0aa84e6d-cbd2-46d4-a6a2-668523adefe7 · outbound

This paper cites A smooth perceptron algorithm.

Solving Zero-Sum Games with Fewer Matrix-Vector Products A smooth perceptron algorithm

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.332715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.142641Z digest=sha256:7ea0566094f7dda3ab32af8e476bcad0fe7a60ce524b954c7515ca8466b69826

Observation f13e8017-5529-47fd-b5cc-83f540933ec7 · outbound

This paper cites A Note on Preconditioning by Low-Stretch Spanning Trees.

Solving Zero-Sum Games with Fewer Matrix-Vector Products A Note on Preconditioning by Low-Stretch Spanning Trees

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-15T16:41:53.146581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:53.146581Z digest=sha256:866063f5b806c5f9de2a00c8fcc3db2395b181018d74563fe95c8b26359eb105

Observation 0d8b8209-c1e6-4fc3-b3c7-8801f8fbf73c · outbound

This paper cites Tight complexity bounds for optimizing composite objectives.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Tight complexity bounds for optimizing composite objectives

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.318975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.150941Z digest=sha256:af7cb41e0af7fdf86235c96096e51a1b02e54dae4b380fe7f71d86060a8a2bc8

Observation 40950fd6-c7c7-464d-8e7e-ea524aba9535 · outbound

This paper cites Saddle points and accelerated perceptron algorithms.

Solving Zero-Sum Games with Fewer Matrix-Vector Products Saddle points and accelerated perceptron algorithms

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.304398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.154761Z digest=sha256:c5208b037f1709a0cf87df45d1d6b42d0cfc3b90ae899f48458427153f661767

Observation 69f6784b-c54d-48a4-b1bc-27a30501ab04 · outbound

This paper cites On lower iteration complexity bounds for the convex concave saddle point problems.

Solving Zero-Sum Games with Fewer Matrix-Vector Products On lower iteration complexity bounds for the convex concave saddle point problems

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:41:53.290935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:41:53.158993Z digest=sha256:f89d3905684eae3e73adc76bd95800b12ca880992b53af0e4d353ee1a39c193f

Pith citing papers

No inbound Pith citation observations are available.