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Documents authored by Shah, Rikhav

Document
DOI: 10.4230/LIPIcs.FUN.2024.29
Achieving the Highest Possible Elo Rating

Authors: Rikhav Shah

Published in: LIPIcs, Volume 291, 12th International Conference on Fun with Algorithms (FUN 2024)

Abstract
Elo rating systems measure the approximate skill of each competitor in a game or sport. A competitor’s rating increases when they win and decreases when they lose. Increasing one’s rating can be difficult work; one must hone their skills and consistently beat the competition. Alternatively, with enough money you can rig the outcome of games to boost your rating. This paper poses a natural question for Elo rating systems: say you manage to get together n people (including yourself) and acquire enough money to rig k games. How high can you get your rating, asymptotically in k? In this setting, the people you gathered aren't very interested in the game, and will only play if you pay them to. This paper resolves the question for n = 2 up to constant additive error, and provides close upper and lower bounds for all other n, including for n growing arbitrarily with k. There is a phase transition at n = k^{1/3}: there is a huge increase in the highest possible Elo rating from n = 2 to n = k^{1/3}, but (depending on the particular Elo system used) little-to-no increase for any higher n. Past the transition point n > k^{1/3}, the highest possible Elo is at least Θ(k^{1/3}). The corresponding upper bound depends on the particular system used, but for the standard Elo system, is Θ(k^{1/3}log(k)^{1/3}).
Cite as

Rikhav Shah. Achieving the Highest Possible Elo Rating. In 12th International Conference on Fun with Algorithms (FUN 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 291, pp. 29:1-29:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{shah:LIPIcs.FUN.2024.29,
  author =	{Shah, Rikhav},
  title =	{{Achieving the Highest Possible Elo Rating}},
  booktitle =	{12th International Conference on Fun with Algorithms (FUN 2024)},
  pages =	{29:1--29:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-314-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{291},
  editor =	{Broder, Andrei Z. and Tamir, Tami},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FUN.2024.29},
  URN =		{urn:nbn:de:0030-drops-199376},
  doi =		{10.4230/LIPIcs.FUN.2024.29},
  annote =	{Keywords: Elo, rating system, monotonic invariant, Euler’s method, mass movement}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2022.108
A Spectral Approach to Polytope Diameter

Authors: Hariharan Narayanan, Rikhav Shah, and Nikhil Srivastava

Published in: LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)

Abstract
We prove upper bounds on the graph diameters of polytopes in two settings. The first is a worst-case bound for integer polytopes in terms of the length of the description of the polytope (in bits) and the minimum angle between facets of its polar. The second is a smoothed analysis bound: given an appropriately normalized polytope, we add small Gaussian noise to each constraint. We consider a natural geometric measure on the vertices of the perturbed polytope (corresponding to the mean curvature measure of its polar) and show that with high probability there exists a "giant component" of vertices, with measure 1-o(1) and polynomial diameter. Both bounds rely on spectral gaps - of a certain Schrödinger operator in the first case, and a certain continuous time Markov chain in the second - which arise from the log-concavity of the volume of a simple polytope in terms of its slack variables.
Cite as

Hariharan Narayanan, Rikhav Shah, and Nikhil Srivastava. A Spectral Approach to Polytope Diameter. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 108:1-108:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{narayanan_et_al:LIPIcs.ITCS.2022.108,
  author =	{Narayanan, Hariharan and Shah, Rikhav and Srivastava, Nikhil},
  title =	{{A Spectral Approach to Polytope Diameter}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{108:1--108:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.108},
  URN =		{urn:nbn:de:0030-drops-157044},
  doi =		{10.4230/LIPIcs.ITCS.2022.108},
  annote =	{Keywords: Polytope diameter, Markov Chain}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2021.40
Smoothed Analysis of the Condition Number Under Low-Rank Perturbations

Authors: Rikhav Shah and Sandeep Silwal

Published in: LIPIcs, Volume 207, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)

Abstract
Let M be an arbitrary n by n matrix of rank n-k. We study the condition number of M plus a low-rank perturbation UV^T where U, V are n by k random Gaussian matrices. Under some necessary assumptions, it is shown that M+UV^T is unlikely to have a large condition number. The main advantages of this kind of perturbation over the well-studied dense Gaussian perturbation, where every entry is independently perturbed, is the O(nk) cost to store U,V and the O(nk) increase in time complexity for performing the matrix-vector multiplication (M+UV^T)x. This improves the Ω(n²) space and time complexity increase required by a dense perturbation, which is especially burdensome if M is originally sparse. Our results also extend to the case where U and V have rank larger than k and to symmetric and complex settings. We also give an application to linear systems solving and perform some numerical experiments. Lastly, barriers in applying low-rank noise to other problems studied in the smoothed analysis framework are discussed.
Cite as

Rikhav Shah and Sandeep Silwal. Smoothed Analysis of the Condition Number Under Low-Rank Perturbations. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 207, pp. 40:1-40:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{shah_et_al:LIPIcs.APPROX/RANDOM.2021.40,
  author =	{Shah, Rikhav and Silwal, Sandeep},
  title =	{{Smoothed Analysis of the Condition Number Under Low-Rank Perturbations}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)},
  pages =	{40:1--40:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-207-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{207},
  editor =	{Wootters, Mary and Sanit\`{a}, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2021.40},
  URN =		{urn:nbn:de:0030-drops-147332},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2021.40},
  annote =	{Keywords: Smoothed analysis, condition number, low rank noise}
}
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