REVIEW 6 minor 117 references
Online Koml\'os converges to mean curvature flow
T0 review · 0 major / 6 minor · reviewed 2026-07-13 · grok-4.5
Pith's one-line read The large-T value of the online Komlós game is set by the extinction time of the unit cube under mean curvature flow.
desk verdict Clean continuum limit for the adaptive online Komlós game: value ~ √(T/(2τ)) with τ the extinction time under the natural curvature flow, plus matching √log m bounds for the cube. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The rescaled discrete value function w_δ(x,t)=δ v(x/δ,t/δ^{2}) of the online game, which converges locally uniformly to the unique viscosity solution of the level-set PDE for the relevant curvature flow; the limiting nonlinear operator is identified via Banaszczyk’s theorem.
What would settle it
Compute the exact extinction time of the unit cube under mean curvature flow for moderate dimension m and check whether K_T(m,m-1)/√T approaches 1/√(2τ) for large T; a persistent discrepancy would refute the claimed limit.
Extended reading notes
Core claim
For every centrally symmetric convex body E the asymptotic value of the online Komlós game satisfies lim T o∞ K_{n,T}(E)/√T = 1/√(2 τ_n(E)), where τ_n(E) is the extinction time of E under the flow whose normal velocity equals the sum of the largest min(n,m-1) principal curvatures. When n≥m-1 and E is the unit cube this constant is Θ(√log m).
Load-bearing premise
That the half-relaxed limits of the discrete value functions are viscosity sub- and supersolutions of the continuum curvature PDE, so that the comparison principle forces them to coincide.
Editorial extensions
If this is right
- The dimensional growth of the large-T value is completely determined by the Gaussian width of the dual body and by the maximal dual basis vectors.
- Any centrally symmetric norm, not merely the ℓ∞ norm, has an online Komlós asymptotic given by the same geometric extinction time.
- When the batch size n is smaller than m-1 the continuum limit is flow by the sum of the largest n principal curvatures rather than mean curvature flow.
- Upper and lower bounds on extinction times for convex bodies become upper and lower bounds on the asymptotic value of the corresponding online balancing game.
Reading between the lines
- The continuum description may supply a new route to lower bounds for the classic (T=1) Komlós problem by examining the short-time expansion of the same PDE.
- The same viscosity machinery should apply to online balancing against oblivious or stochastic adversaries once the corresponding Bellman operators are identified.
- Explicit barriers built from the heat equation give the first analytic estimates of the extinction time of the cube under mean curvature flow that are sharp up to the constant √2.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper determines the large-T asymptotics of the online Komlós game: Paul presents batches of n unit-ball vectors in R^m and Carol chooses signs, with the value K_{n,T}(E) equal to the resulting ||sum||_E after T rounds. Theorem 2.1 states that lim_{T o∞} K_{n,T}(E)/√T = 1/√(2 τ_n(E)), where τ_n(E) is the extinction time of the centrally symmetric convex body E under the flow whose normal speed is the sum of the largest min(n,m−1) principal curvatures. When n≥m−1 this is mean curvature flow; for the cube one obtains the dimensional bounds (1−o(1))√log m ≤ lim K_T(m,n)/√T ≤ (√2+o(1))√log m (Theorem 1.2 / 2.2). The argument constructs a dynamic-programming value function, rescales it to a finite-difference scheme, identifies the continuum Bellman operator via Banaszczyk’s ℓ² discrepancy theorem, passes to half-relaxed limits that are viscosity sub- and supersolutions, and invokes comparison plus convexity preservation to obtain uniqueness; non-smooth bodies are recovered by sandwich approximation.
Significance. The work supplies a clean continuum limit that localizes the classic Komlós problem to a curvature-driven geometric evolution, thereby importing the full apparatus of viscosity solutions and comparison principles into online vector balancing. The identification of the discrete max-min with the sum of the largest positive projected eigenvalues (Lemmas 5.4–5.6) is a non-trivial extension of the Kohn–Serfaty framework beyond orthonormal frames, made possible by Banaszczyk. The resulting extinction-time bounds for the cube under mean curvature flow are of independent geometric interest and are obtained by elementary heat-equation barriers. The proofs are self-contained once standard viscosity machinery is granted, and the statements are parameter-free. Even if the hoped-for feedback into the offline Komlós conjecture remains unrealized, the localization perspective and the precise constant 1/√(2τ) constitute a substantial contribution at the interface of combinatorial discrepancy and geometric PDE.
minor comments (6)
- Notation for the game value oscillates between K_T(m,n) (Introduction) and K_{n,T}(E) (Section 2 onward). A single consistent symbol would help the reader.
- In the elementary strategy bounds (3.1) the constant c(m,n)=min(n,m−1)/(min(n,m−1)+1) is stated without a one-line derivation that |y_t|≤√(n*+1)∥y_t∥_∞; adding it would make the comparison with the continuum constant immediate.
- Lemma 5.4 asserts sharpness of both half-relaxed envelopes at p=0 by exhibiting sequences; a short explicit construction for the liminf sequence (beyond the sketch with p_k=√δ_k σ_0) would make the claim fully self-contained.
- Appendix B cites Giga–Goto–Ishii–Sato for the comparison principle under linear growth. A one-sentence verification that the structural hypotheses (degenerate ellipticity, F^*=F_* at the origin, local bound on |F|) hold for H_n would spare the reader a cross-check.
- Figures 1 and 2 are conceptually helpful but lack axis labels and a precise caption statement of the ambient dimension and batch size; adding these would improve accessibility.
- Typographical: “Koml´os” appears with inconsistent accent placement in a few places (e.g., page 2); standardize throughout.
Circularity Check
No significant circularity: continuum limit of discrete game value is derived independently of the geometric extinction time.
full rationale
The derivation is a standard viscosity-solution continuum limit (rescaled value function w_δ solves a finite-difference scheme whose consistency limit is the level-set PDE for curvature flow; half-relaxed limits are sub/supersolutions by Lemmas 5.4/5.6 + 6.5; uniqueness via external comparison of Giga–Goto–Ishii–Sato). The extinction time τ_n(E) is defined purely geometrically (2.1) and is related to the PDE solution only after the limit is established (via self-similarity Lemma B.3 and convexity preservation Theorem B.4, both external). Banaszczyk is used only to identify the Bellman operator with the sum of largest positive eigenvalues of the projected Hessian; no parameter is fitted to game values, no uniqueness theorem is imported from the authors’ prior work, and the heat-equation barriers for the dimensional bounds are independent comparison arguments. The argument is self-contained against external geometric and PDE benchmarks.
Assumptions & free parameters
assumptions (4)
- standard math Banaszczyk’s ℓ² discrepancy theorem (Theorem 5.1): for any ellipsoid D the signed sum of vectors inside D has Euclidean length at most the sum of squared semi-axes.
- standard math Comparison principle for viscosity solutions of geometric parabolic equations (Giga–Goto–Ishii–Sato).
- standard math Preservation of convexity of sublevel sets under the curvature flow (Huisken / Evans–Spruck / Andrews).
- standard math Existence of a unique viscosity solution to the terminal-value problem for the level-set curvature flow with continuous terminal data of linear growth.
Cite this review
Pith. "Pith review of Online Koml\'os converges to mean curvature flow." pith.science (2026). https://pith.science/paper/BQ3X6QIG
@misc{pith2026260708943,
author = {Pith},
title = {Pith review of: Online Koml\'os converges to mean curvature flow},
year = {2026},
howpublished = {\url{https://pith.science/paper/BQ3X6QIG}},
note = {Machine review of arXiv:2607.08943}
}
abstract
We determine the asymptotics of a game inspired by classic vector balancing problems in combinatorial discrepancy theory. In this game, which we call the online Koml\'os game, two players, Paul and Carol, update the state vector $y$ in $\mathbb{R}^m$, initially placed at $0$. At each round, Paul chooses freely a set of $n$ vectors in the Euclidean unit ball, and Carol chooses, for each such vector, whether to leave it unchanged or reverse its sign. The resulting vectors are all added to $y$, and the game proceeds to a new round. After $T$ rounds, the game ends, and the $\ell_\infty$ norm of the state vector $y$ is determined. Paul's objective throughout the game is to maximize this norm, and Carol's objective is to minimize it. As $T$ gets large, we establish that the leading order term of the value of this game is $\sqrt{T/2\tau}$, where $\tau$ is the extinction time of the unit cube in $\mathbb{R}^m$ under a curvature-based flow characterized by the values of $m$ and $n$. When $n\geq m-1$, this flow is the mean curvature flow, and we show that $1/\sqrt{2\tau} =\Theta(\sqrt{\log m})$. Our results build upon the work of Kohn and Serfaty on deterministic games and mean curvature flow, combined with Banaszczyk's $\ell^2$ analogue of the Beck-Fiala theorem. As the large $T$ limit of the online Koml\'os game amounts to a localization of the classic Koml\'os problem, we hope this work can shed light on this and other vector balancing problems. Our results generalize to the version of the online Koml\'os game with the final value given by an arbitrary norm in $\mathbb{R}^m$.
Figures
Reference graph
Works this paper leans on
-
[1]
Smoothed analysis of the Koml´ os conjecture: Rademacher noise
Elad Aigner-Horev, Dan Hefetz, and Michael Trushkin. Smoothed analysis of the Koml´ os conjecture: Rademacher noise. The Electronic Journal of Combinatorics, 32(1):P1.52, Mar. 2025
2025
-
[2]
On the structure of bad science matrices, 2025
Alex Albors, Hisham Bhatti, Lukshya Ganjoo, Raymond Guo, Dmitriy Kunisky, Rohan Mukherjee, Alicia Stepin, and Tony Zeng. On the structure of bad science matrices, 2025
2025
-
[3]
Spencer.The Probabilistic Method
Noga Alon and Joel H. Spencer.The Probabilistic Method. John Wiley & Sons, Inc., 2000
2000
-
[4]
Altschuler and Jonathan Niles-Weed
Dylan J. Altschuler and Jonathan Niles-Weed. The discrepancy of random rectangular matrices.Random Structures & Algorithms, 60(4):551–593, 2022
2022
-
[5]
Altschuler and Konstantin Tikhomirov
Dylan J. Altschuler and Konstantin Tikhomirov. A threshold for online balancing of sparse i.i.d. vectors, 2025
2025
-
[6]
Liu, and Mehtaab Sawhney
Ryan Alweiss, Yang P. Liu, and Mehtaab Sawhney. Discrepancy minimization via a self-balancing walk. InProceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, STOC 2021, page 14–20, New York, NY, USA,
2021
-
[7]
Association for Computing Machinery
-
[8]
Contraction of convex hypersurfaces in Euclidean space.Calculus of Variations and Partial Differential Equations, 2(2):151–171, 1994
Ben Andrews. Contraction of convex hypersurfaces in Euclidean space.Calculus of Variations and Partial Differential Equations, 2(2):151–171, 1994
1994
Show all 117 references
-
[9]
Storage capacity in symmetric binary perceptrons.Journal of Physics A: Mathematical and Theoretical, 52(29):294003, June 2019
Benjamin Aubin, Will Perkins, and Lenka Zdeborov´ a. Storage capacity in symmetric binary perceptrons.Journal of Physics A: Mathematical and Theoretical, 52(29):294003, June 2019
2019
-
[10]
A Beck—Fiala-type theorem for Euclidean norms.European Journal of Combinatorics, 11(6):497– 500, 1990
Wojciech Banaszczyk. A Beck—Fiala-type theorem for Euclidean norms.European Journal of Combinatorics, 11(6):497– 500, 1990
1990
-
[11]
Balancing vectors and convex bodies.Studia Mathematica, 106(1):93–100, 1993
Wojciech Banaszczyk. Balancing vectors and convex bodies.Studia Mathematica, 106(1):93–100, 1993
1993
-
[12]
Balancing vectors and Gaussian measures of n-dimensional convex bodies.Random Structures & Algorithms, 12(4):351–360, 1998
Wojciech Banaszczyk. Balancing vectors and Gaussian measures of n-dimensional convex bodies.Random Structures & Algorithms, 12(4):351–360, 1998
1998
-
[13]
Bandeira and Helmut B¨ olcskei
Afonso S. Bandeira and Helmut B¨ olcskei. Matrix discrepancy for representations of finite groups, 2026
2026
-
[14]
Bandeira, Dmitriy Kunisky, Dustin G
Afonso S. Bandeira, Dmitriy Kunisky, Dustin G. Mixon, and Xinmeng Zeng. On the concentration of Gaussian Cayley matrices.Applied and Computational Harmonic Analysis, 73:101694, 2024
2024
-
[15]
A remark on Kashin’s discrepancy argument and partial coloring in the Koml´ os conjecture.Portugaliae Mathematica, 79(3):311–316, 2022
Afonso S Bandeira, Antoine Maillard, and Nikita Zhivotovskiy. A remark on Kashin’s discrepancy argument and partial coloring in the Koml´ os conjecture.Portugaliae Mathematica, 79(3):311–316, 2022
2022
-
[16]
Constructive algorithms for discrepancy minimization
Nikhil Bansal. Constructive algorithms for discrepancy minimization. In2010 IEEE 51st Annual Symposium on Foun- dations of Computer Science, pages 3–10, 2010
2010
-
[17]
Discrepancy theory and related algorithms
Nikhil Bansal. Discrepancy theory and related algorithms. InProc. Int. Cong. Math, volume 7, pages 5178–5210. Inter- national Mathematical Union, 2022
2022
-
[18]
An algorithm for Koml´ os conjecture matching Banaszczyk’s bound
Nikhil Bansal, Daniel Dadush, and Shashwat Garg. An algorithm for Koml´ os conjecture matching Banaszczyk’s bound. SIAM Journal on Computing, 48(2):534–553, 2019
2019
-
[19]
The Gram–Schmidt walk: A cure for the Banaszczyk blues.Theory of Computing, 15(21):1–27, 2019
Nikhil Bansal, Daniel Dadush, Shashwat Garg, and Shachar Lovett. The Gram–Schmidt walk: A cure for the Banaszczyk blues.Theory of Computing, 15(21):1–27, 2019
2019
-
[20]
Decoupling via affine spectral-independence: Beck-Fiala and Koml´ os bounds beyond Banaszczyk
Nikhil Bansal and Haotian Jiang. Decoupling via affine spectral-independence: Beck-Fiala and Koml´ os bounds beyond Banaszczyk. InProceedings of the 58th Annual ACM SIGACT Symposium on Theory of Computing, to appear, STOC 2026, New York, NY, USA, 2026. Association for Computin...
2026
-
[21]
Online discrepancy minimization for stochastic arrivals
Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, and Makrand Sinha. Online discrepancy minimization for stochastic arrivals. InProceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 2842– 2861
2021
-
[22]
Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector Balancing
Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, and Makrand Sinha. Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector Balancing. In Mark Braverman, editor,13th Innovations in Theoretical Computer Science Conference (ITCS 2022), volume 215 ofLeibniz Interna...
2022
-
[23]
Smoothed Analysis of the Koml´ os Con- jecture
Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, and Makrand Sinha. Smoothed Analysis of the Koml´ os Con- jecture. In Miko laj Boja´ nczyk, Emanuela Merelli, and David P. Woodruff, editors,49th International Colloquium on Automata, Languages, and Programming (ICALP 202...
2022
-
[24]
Online vector balancing and geometric discrepancy
Nikhil Bansal, Haotian Jiang, Sahil Singla, and Makrand Sinha. Online vector balancing and geometric discrepancy. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, STOC 2020, page 1139–1152, New York, NY, USA, 2020. Association for Computing Machinery
2020
-
[25]
A Unified Approach to Discrepancy Minimization
Nikhil Bansal, Aditi Laddha, and Santosh Vempala. A Unified Approach to Discrepancy Minimization. In Amit Chakrabarti and Chaitanya Swamy, editors,Approximation, Randomization, and Combinatorial Optimization. Algo- rithms and Techniques (APPROX/RANDOM 2022), volume 245 ofLeibn...
2022
-
[26]
Nikhil Bansal and Joel H. Spencer. On-line balancing of random inputs.Random Structures & Algorithms, 57(4):879–891, 2020
2020
-
[27]
Springer, 1997
Martino Bardi, Italo Capuzzo Dolcetta, et al.Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equa- tions, volume 12. Springer, 1997
1997
-
[28]
Barles and P
G. Barles and P. E. Souganidis. Convergence of approximation schemes for fully nonlinear second order equations.As- ymptotic Anal., 4(3):271–283, 1991
1991
-
[29]
Integer-making
J´ ozsef Beck and Tibor Fiala. “Integer-making” theorems.Discrete Applied Mathematics, 3(1):1–8, 1981
1981
-
[30]
Some remarks on the Gram-Schmidt walk algorithm and consequences for the Koml´ os conjecture
Witold Bednorz and Piotr Godlewski. Some remarks on the Gram-Schmidt walk algorithm and consequences for the Koml´ os conjecture. In Rados law Adamczak, Nathael Gozlan, Karim Lounici, Mokshay Madiman, Florence Merlev` ede, and Elisabeth Werner, editors,High Dimensional Probabi...
2026
-
[31]
Princeton Landmarks in Mathematics
Richard Bellman.Dynamic programming. Princeton Landmarks in Mathematics. Princeton University Press, Princeton, NJ, 2010. Reprint of the 1957 edition, With a new introduction by Stuart Dreyfus
2010
-
[32]
Brakke.The motion of a surface by its mean curvature
Kenneth A. Brakke.The motion of a surface by its mean curvature. Mathematical Notes. Princeton Univ. Press, Princeton, NJ, 1978
1978
-
[33]
A representation formula for the mean curvature motion
Rainer Buckdahn, Pierre Cardaliaguet, and Marc Quincampoix. A representation formula for the mean curvature motion. SIAM Journal on Mathematical Analysis, 33(4):827–846, 2001
2001
-
[34]
Lecture notes on viscosity solutions, 2024
Jeff Calder. Lecture notes on viscosity solutions, 2024. Available athttps://www-users.cse.umn.edu/ ~jwcalder/ viscosity_solutions.pdf
2024
-
[35]
Karthekeyan Chandrasekaran and Santosh S. Vempala. Integer feasibility of random polytopes: random integer programs. InProceedings of the 5th Conference on Innovations in Theoretical Computer Science, ITCS ’14, page 449–458, New York, NY, USA, 2014. Association for Computing Machinery
2014
-
[36]
Tight hardness results for minimizing discrepancy
Moses Charikar, Alantha Newman, and Aleksandar Nikolov. Tight hardness results for minimizing discrepancy. InPro- ceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’11, page 1607–1614, USA, 2011. Society for Industrial and Applied Mathematics
2011
-
[37]
A mixed problem for the infinity laplacian via tug-of-war games.Calculus of Variations and Partial Differential Equations, 34(3):307–320, 2009
Fernando Charro, Jesus Garc´ ıa Azorero, and Julio D Rossi. A mixed problem for the infinity laplacian via tug-of-war games.Calculus of Variations and Partial Differential Equations, 34(3):307–320, 2009
2009
-
[38]
A note on norms of signed sums of vectors.Advances in Geometry, 21(1):5–14, 2021
Giorgos Chasapis and Nikos Skarmogiannis. A note on norms of signed sums of vectors.Advances in Geometry, 21(1):5–14, 2021
2021
-
[39]
Cambridge University Press, 2000
Bernard Chazelle.The Discrepancy Method: Randomness and Complexity. Cambridge University Press, 2000
2000
-
[40]
J. Cheeger. A lower bound for the smallest eigenvalue of the Laplacian. Probl. Analysis, Sympos. in Honor of Salomon Bochner, Princeton Univ. 1969, 195-199 (1970)., 1970
1969
-
[41]
Springer Cham, 2014
William Chen, Anand Srivastav, and Giancarlo Travaglini, editors.A Panorama of Discrepancy Theory. Springer Cham, 2014
2014
-
[42]
Uniqueness and existence of viscosity solutions of generalized mean curvature flow equations.Proc
Yun Gang Chen, Yoshikazu Giga, and Shun’ichi Goto. Uniqueness and existence of viscosity solutions of generalized mean curvature flow equations.Proc. Japan Acad. Ser. A Math. Sci., 65(7):207–210, 1989
1989
-
[43]
Gaussian discrepancy: A probabilistic relaxation of vector balancing.Discrete Applied Mathematics, 322:123–141, 2022
Sinho Chewi, Patrik Gerber, Philippe Rigollet, and Paxton Turner. Gaussian discrepancy: A probabilistic relaxation of vector balancing.Discrete Applied Mathematics, 322:123–141, 2022
2022
-
[44]
Mean curvature flow.Bulletin of the American Mathematical Society, 52(2):297–333, 2015
Tobias Colding, William Minicozzi, Erik Pedersen, et al. Mean curvature flow.Bulletin of the American Mathematical Society, 52(2):297–333, 2015
2015
-
[45]
Costello
Kevin P. Costello. Balancing Gaussian vectors.Israel Journal of Mathematics, 172(1):145–156, 2009
2009
-
[46]
Sur la forme int´ egro-diff´ erentielle des op´ erateurs dec∞ k danscsatisfaisant au principe du maximum
Philippe Courrege. Sur la forme int´ egro-diff´ erentielle des op´ erateurs dec∞ k danscsatisfaisant au principe du maximum. S´ eminaire de th´ eorie du potentiel, 10(1):1–38, 1965
1965
-
[47]
User’s guide to viscosity solutions of second order partial differential equations.Bulletin of the American mathematical society, 27(1):1–67, 1992
Michael G Crandall, Hitoshi Ishii, and Pierre-Louis Lions. User’s guide to viscosity solutions of second order partial differential equations.Bulletin of the American mathematical society, 27(1):1–67, 1992
1992
-
[48]
Anirban DasGupta, S. N. Lahiri, and Jordan Stoyanov. Sharp fixednbounds and asymptotic expansions for the mean and the median of a Gaussian sample maximum, and applications to the Donoho–Jin model.Statistical Methodology, 20:40–62, 2014
2014
-
[49]
Efficient algorithms for discrepancy minimization in convex sets.Random Structures & Algorithms, 53(2):289–307, 2018
Ronen Eldan and Mohit Singh. Efficient algorithms for discrepancy minimization in convex sets.Random Structures & Algorithms, 53(2):289–307, 2018
2018
-
[50]
Motion of level sets by mean curvature
Lawrence C Evans and Joel Spruck. Motion of level sets by mean curvature. I. InFundamental Contributions to the Continuum Theory of Evolving Phase Interfaces in Solids: A Collection of Reprints of 14 Seminal Papers, pages 328–
-
[51]
On the Beck-Fiala conjecture for random set systems.Random Structures & Algorithms, 54(4):665–675, 2019
Esther Ezra and Shachar Lovett. On the Beck-Fiala conjecture for random set systems.Random Structures & Algorithms, 54(4):665–675, 2019
2019
-
[52]
The mean-field limit of online stochastic vector balancing, 2026
Christian Fiedler, Joe Jackson, Daniel Lacker, and Jonathan Niles-Weed. The mean-field limit of online stochastic vector balancing, 2026
2026
-
[53]
Springer, 2006
Wendell H Fleming and H Mete Soner.Controlled Markov processes and viscosity solutions. Springer, 2006. ONLINE KOML ´OS CONVERGES TO MEAN CURVATURE FLOW 31
2006
-
[54]
On the discrepancy of random matrices with many columns.Random Structures & Algorithms, 57(1):64–96, 2020
Cole Franks and Michael Saks. On the discrepancy of random matrices with many columns.Random Structures & Algorithms, 57(1):64–96, 2020
2020
-
[55]
Galambos.Asymptotic Theory of Extreme Order Statistics
J. Galambos.Asymptotic Theory of Extreme Order Statistics. Wiley, New York, 1987
1987
-
[56]
On some vector balancing problems.Studia Mathematica, 122(3):225–234, 1997
Apostolos Giannopoulos. On some vector balancing problems.Studia Mathematica, 122(3):225–234, 1997
1997
-
[57]
Y. Giga, S. Goto, H. Ishii, and M.-H. Sato. Comparison principle and convexity preserving properties for singular degen- erate parabolic equations on unbounded domains.Indiana Univ. Math. J., 40(2):443–470, 1991
1991
-
[58]
Springer, 2006
Yoshikazu Giga.Surface evolution equations: A level set approach. Springer, 2006
2006
-
[59]
Monographs in Mathematics
Yoshikazu Giga and Qing Liu.Surface Evolution Equations: A Level Set Approach. Monographs in Mathematics. Birkh¨ auser Cham, 2 edition, 2026
2026
-
[60]
On a lower bound for the extinction time of surfaces moved by mean curvature
Yoshikazu Giga and Kazuyuki Yama-uchi. On a lower bound for the extinction time of surfaces moved by mean curvature. Calculus of Variations and Partial Differential Equations, 1(4):417–428, 1993
1993
-
[61]
Extremal properties of orthogonal parallelepipeds and their applications to the geometry of Banach spaces.Mathematics of the USSR-Sbornik, 64(1):85–96, 1989
Efim Davydovich Gluskin. Extremal properties of orthogonal parallelepipeds and their applications to the geometry of Banach spaces.Mathematics of the USSR-Sbornik, 64(1):85–96, 1989
1989
-
[62]
Rossi, and Jorge Ruiz-Cases
Irene Gonzalvez, Alfredo Miranda, Julio D. Rossi, and Jorge Ruiz-Cases. A two-player zero-sum probabilistic game that approximates the mean curvature flow.Communications in Mathematics, Volume 34 (2026), Issue 2 (Special issue: Latin American mathematics), Jul 2025
2026
-
[63]
Nestor Guillen and Russell W. Schwab. Min-max formulas for nonlocal elliptic operators.Calc. Var. Partial Differential Equations, 58(6):Paper No. 209, 79, 2019
2019
-
[64]
Nestor Guillen and Russell W. Schwab. Min-max formulas for nonlocal elliptic operators on Euclidean space.Nonlinear Anal., 193:111468, 51, 2020
2020
-
[65]
Sinan G¨ unt¨ urk
C. Sinan G¨ unt¨ urk. Mathematics of analog-to-digital conversion.Communications on Pure and Applied Mathematics, 65(12):1671–1696, 2012
2012
-
[66]
Smoothed analysis with adaptive adversaries.J
Nika Haghtalab, Tim Roughgarden, and Abhishek Shetty. Smoothed analysis with adaptive adversaries.J. ACM, 71(3), June 2024
2024
-
[67]
On a conjecture of Komlos about signed sums of vectors inside the sphere.European Journal of Combinatorics, 9(1):33–37, 1988
D Hajela. On a conjecture of Komlos about signed sums of vectors inside the sphere.European Journal of Combinatorics, 9(1):33–37, 1988
1988
-
[68]
Spielman, and Peng Zhang
Christopher Harshaw, Fredrik S¨ avje, Daniel A. Spielman, and Peng Zhang. Balancing covariates in randomized experi- ments with the Gram–Schmidt walk design.Journal of the American Statistical Association, 119(548):2934–2946, 2024
2024
-
[69]
A Fourier-analytic approach for the discrepancy of random set systems
Rebecca Hoberg and Thomas Rothvoss. A Fourier-analytic approach for the discrepancy of random set systems. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’19, page 2547–2556, USA,
-
[70]
Society for Industrial and Applied Mathematics
-
[71]
Flow by mean curvature of convex surfaces into spheres.Journal of Differential Geometry, 20(1):237– 266, 1984
Gerhard Huisken. Flow by mean curvature of convex surfaces into spheres.Journal of Differential Geometry, 20(1):237– 266, 1984
1984
-
[72]
Repeated games for non-linear parabolic integro-differential equations and integral cur- vature flows.Discrete and Continuous Dynamical Systems, 29(4):1517–1552, 2011
Cyril Imbert and Sylvia Serfaty. Repeated games for non-linear parabolic integro-differential equations and integral cur- vature flows.Discrete and Continuous Dynamical Systems, 29(4):1517–1552, 2011
2011
-
[73]
Online geometric discrepancy for stochastic arrivals with applica- tions to envy minimization, 2019
Haotian Jiang, Janardhan Kulkarni, and Sahil Singla. Online geometric discrepancy for stochastic arrivals with applica- tions to envy minimization, 2019
2019
-
[74]
Bounds on the expectation of the maximum of samples from a Gaussian, 1998
Gautam Kamath. Bounds on the expectation of the maximum of samples from a Gaussian, 1998
1998
-
[75]
B. S. Kashin. On an isometric operator inL 2(0,1).C. R. Acad. Bulg. Sci., 38:1613–1615, 1985
1985
-
[76]
Vladimir A. Kobzar. The symmetric two-armed bandit: the general case, 2026
2026
-
[77]
Kobzar and Robert V
Vladimir A. Kobzar and Robert V. Kohn. A PDE-based analysis of the symmetric two-armed Bernoulli bandit, 2022
2022
-
[78]
Kobzar, Robert V
Vladimir A. Kobzar, Robert V. Kohn, and Zhilei Wang. New potential-based bounds for prediction with expert advice. In Jacob Abernethy and Shivani Agarwal, editors,Proceedings of the 33rd Annual Conference on Learning Theory (COLT), volume 125 ofProceedings of Machine Learning ...
2020
-
[79]
Kobzar, Robert V
Vladimir A. Kobzar, Robert V. Kohn, and Zhilei Wang. New potential-based bounds for the geometric-stopping version of prediction with expert advice. In Jianfeng Lu and Rachel Ward, editors,Proceedings of the 1st Annual Conference on Mathematical and Scientific Machine Learning...
2020
-
[80]
Robert Kohn and Sylvia Serfaty. A deterministic-control-based approach to motion by curvature.Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 59(3):344–407, 2006
2006
-
[81]
Kohn and Sylvia Serfaty
Robert V. Kohn and Sylvia Serfaty. Second-order PDE’s and deterministic games. InICIAM 07 : 6th International Conference on Industrial and Applied Mathematics, Zurich, Switzerland, July 2007
2007
-
[82]
Kohn and Sylvia Serfaty
Robert V. Kohn and Sylvia Serfaty. A deterministic-control-based approach to fully nonlinear parabolic and elliptic equations.Communications on Pure and Applied Mathematics, 63:1298–1350, 2010
2010
-
[83]
Optimal online discrepancy minimization
Janardhan Kulkarni, Victor Reis, and Thomas Rothvoss. Optimal online discrepancy minimization. InProceedings of the 56th Annual ACM Symposium on Theory of Computing, STOC 2024, page 1832–1840, New York, NY, USA, 2024. Association for Computing Machinery
2024
-
[84]
The discrepancy of unsatisfiable matrices and a lower bound for the Koml´ os conjecture constant.SIAM Journal on Discrete Mathematics, 37(2):586–603, 2023
Dmitriy Kunisky. The discrepancy of unsatisfiable matrices and a lower bound for the Koml´ os conjecture constant.SIAM Journal on Discrete Mathematics, 37(2):586–603, 2023
2023
-
[85]
Asymptotic bounds and online algorithms for average-case matrix discrepancy, 2025
Dmitriy Kunisky, Timm Oertel, Nicola Wengiel, and Peiyuan Zhang. Asymptotic bounds and online algorithms for average-case matrix discrepancy, 2025
2025
-
[86]
Minimum curvature flow and martingale exit times.Electronic Journal of Probability, 29:1–32, 2024
Martin Larsson and Johannes Ruf. Minimum curvature flow and martingale exit times.Electronic Journal of Probability, 29:1–32, 2024. 32 NESTOR GUILLEN AND VLADIMIR A. KOBZAR
2024
-
[87]
Deterministic discrepancy minimization via the multiplicative weight update method
Avi Levy, Harishchandra Ramadas, and Thomas Rothvoss. Deterministic discrepancy minimization via the multiplicative weight update method. In Friedrich Eisenbrand and Jochen Koenemann, editors,Integer Programming and Combinatorial Optimization, pages 380–391, Cham, 2017. Spring...
2017
-
[88]
A game-theoretic proof of convexity-preserving properties for motion by curvature.Indiana Univ
Qing Liu, Armin Schikorra, and Xiaodan Zhou. A game-theoretic proof of convexity-preserving properties for motion by curvature.Indiana Univ. Math. J., 65(1):171–197, 2016
2016
-
[89]
Y. Lonke. Combinatorial problems in finite dimensional normed spaces (PhD dissertation, Hebrew university), 1998
1998
-
[90]
Constructive discrepancy minimization by walking on the edges.SIAM Journal on Computing, 44(5):1573–1582, 2015
Shachar Lovett and Raghu Meka. Constructive discrepancy minimization by walking on the edges.SIAM Journal on Computing, 44(5):1573–1582, 2015
2015
-
[91]
Springer Berlin, Heidelberg, 1999
Jiˇ r´ ı Matouˇ sek.Geometric Discrepancy. Springer Berlin, Heidelberg, 1999
1999
-
[92]
Factorization norms and hereditary discrepancy.International Mathematics Research Notices, 2020(3):751–780, 02 2020
Jiˇ r´ ı Matouˇ sek, Aleksandar Nikolov, and Kunal Talwar. Factorization norms and hereditary discrepancy.International Mathematics Research Notices, 2020(3):751–780, 02 2020
2020
-
[93]
On the role of convexity in isoperimetry, spectral gap and concentration.Inventiones mathematicae, 177:1 – 43, 2009
Emanuel Milman. On the role of convexity in isoperimetry, spectral gap and concentration.Inventiones mathematicae, 177:1 – 43, 2009
2009
-
[94]
The geometry of differential privacy: the sparse and approximate cases
Aleksandar Nikolov, Kunal Talwar, and Li Zhang. The geometry of differential privacy: the sparse and approximate cases. InProceedings of the Forty-Fifth Annual ACM Symposium on Theory of Computing, STOC ’13, page 351–360, New York, NY, USA, 2013. Association for Computing Machinery
2013
-
[95]
Optimal non-asymptotic lower bound on the minimax regret of learning with expert advice, 2015
Francesco Orabona and D´ avid P´ al. Optimal non-asymptotic lower bound on the minimax regret of learning with expert advice, 2015. Available athttps://arxiv.org/abs/1511.02176
2015 arXiv
-
[96]
Applied Mathematical Sciences
Stanley Osher and Ronald Fedkiw.Level Set Methods and Dynamic Implicit Surfaces. Applied Mathematical Sciences. Springer New York, NY, 2002
2002
-
[97]
Tug-of-war and the infinity Laplacian.Journal of the American Mathematical Society, 22(1):167–210, 2009
Yuval Peres, Oded Schramm, Scott Sheffield, and David Wilson. Tug-of-war and the infinity Laplacian.Journal of the American Mathematical Society, 22(1):167–210, 2009
2009
-
[98]
Discrepancy minimization via regularization
Lucas Pesenti and Adrian Vladu. Discrepancy minimization via regularization. InProceedings of the 2023 Annual ACM- SIAM Symposium on Discrete Algorithms (SODA), pages 1734–1758. SIAM, 2023
2023
-
[99]
L. S. Pontryagin. The mathematical theory of optimal processes and differential games. volume 169, pages 119–158, 254–255. 1985. Topology, ordinary differential equations, dynamical systems
1985
-
[100]
Constructive discrepancy minimization for convex sets.SIAM Journal on Computing, 46(1):224–234, 2017
Thomas Rothvoss. Constructive discrepancy minimization for convex sets.SIAM Journal on Computing, 46(1):224–234, 2017
2017
-
[101]
Viscosity solutions of elliptic equations, 2015
Luis Silvestre. Viscosity solutions of elliptic equations, 2015. Available athttps://www.math.uchicago.edu/ ~luis/ preprints/viscosity-solutions.pdf
2015
-
[102]
The structure of extremal bad science matrices, 2025
Shridhar Sinha. The structure of extremal bad science matrices, 2025
2025
-
[103]
Discrepancy and Fisher information, 2026
Gleb Smirnov and Roman Vershynin. Discrepancy and Fisher information, 2026
2026
-
[104]
Dynamic programming for stochastic target problems and geometric flows.Journal of the European Mathematical Society, 4(3):201–236, 2002
H Mete Soner and Nizar Touzi. Dynamic programming for stochastic target problems and geometric flows.Journal of the European Mathematical Society, 4(3):201–236, 2002
2002
-
[105]
A stochastic representation for mean curvature type geometric flows.The Annals of probability, 31(3):1145–1165, 2003
H Mete Soner and Nizar Touzi. A stochastic representation for mean curvature type geometric flows.The Annals of probability, 31(3):1145–1165, 2003
2003
-
[106]
Balancing games.Journal of Combinatorial Theory, Series B, 23(1):68–74, 1977
Joel Spencer. Balancing games.Journal of Combinatorial Theory, Series B, 23(1):68–74, 1977
1977
-
[107]
Six standard deviations suffice.Transactions of the American Mathematical Society, 289(2):679–706, 1985
Joel Spencer. Six standard deviations suffice.Transactions of the American Mathematical Society, 289(2):679–706, 1985
1985
-
[108]
Balancing vectors in the max norm.Combinatorica, 6(1):55–65, 1986
Joel Spencer. Balancing vectors in the max norm.Combinatorica, 6(1):55–65, 1986
1986
-
[109]
Society for Industrial and Applied Mathematics, 2nd edition edition, 1994
Joel Spencer.Ten Lectures on the Probabilistic Method. Society for Industrial and Applied Mathematics, 2nd edition edition, 1994
1994
-
[110]
Spielman and Shang-Hua Teng
Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time.J. ACM, 51(3):385–463, May 2004
2004
-
[111]
Bad science matrices, 2024
Stefan Steinerberger. Bad science matrices, 2024
2024
-
[112]
Springer, 2019
Yoshihiro Tonegawa.Brakke’s Mean Curvature Flow: An Introduction. Springer, 2019
2019
-
[113]
London Mathematical Society Stu- dent Texts
Giancarlo Travaglini.Number Theory, Fourier Analysis and Geometric Discrepancy. London Mathematical Society Stu- dent Texts. Cambridge University Press, 2014
2014
-
[114]
Global Comparison Property
Paxton Turner, Raghu Meka, and Philippe Rigollet. Balancing Gaussian vectors in high dimension. In Jacob Abernethy and Shivani Agarwal, editors,Proceedings of Thirty Third Conference on Learning Theory, volume 125 ofProceedings of Machine Learning Research, pages 3455–3486. PM...
2020
-
[115]
There is aK >0such that for allx∈R m, t∈[−T,0]we have u(x, t)≤K(|x|+ 1), v(x, t)≥ −K(|x|+ 1)
-
[116]
The functionsu(x, t), v(x, t)are continuous ast→0, that is, we haveu ∗(x,0) =u ∗(x,0)andv ∗(x,0) = v∗(x,0)for everyx∈R m
-
[117]
positive
For some modulus of continuityρwe have u(x,0)−v(y,0)≤ρ(|x−y|). Then, we have u(x, t)≤v(x, t)∀x∈R m, t∈[−T,0]. Proof.This is a special case of [56, Theorem 3.2], a result that deals with general unbounded domainU (here we only need it forU=R m) and a more general PDEF(D 2u,∇u) ...
Reviewed July 13, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.