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arxiv: 1906.09073 · v1 · pith:SZ4CU3ROnew · submitted 2019-06-21 · 💻 cs.DC

MinMax Algorithms for Stabilizing Consensus

Pith reviewed 2026-05-25 18:51 UTC · model grok-4.3

classification 💻 cs.DC
keywords stabilizing consensusMinMax algorithmtime-varying topologydynamic networksroot agentanonymous agentssynchronous distributed systemsinput stabilization
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The pith

A generic MinMax algorithm solves stabilizing consensus in time-varying networks with periodic roots.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that stabilizing consensus can be achieved in synchronous networks of identical anonymous agents connected by changing links. Agents start with input values and must eventually all output the same one of those inputs. The algorithm succeeds whenever every bounded time interval contains a root agent that can reach every other agent, possibly through a chain of messages and with the root changing from interval to interval. No central coordinator or knowledge of network size is required.

Core claim

Our main result is a generic MinMax algorithm that solves the stabilizing consensus problem in this model when, in each sufficiently long but bounded period of time, there is an agent, called a root, that can send messages, possibly indirectly, to all the agents. Such topologies are highly dynamic (in particular, roots may change arbitrarily over time) and enforce no strong connectivity property (an agent may be never a root). Our distributed MinMax algorithms require neither central control (e.g., synchronous starts) nor any global information (e.g., on the size of the network), and are quite efficient in terms of message size and storage requirements.

What carries the argument

The generic MinMax algorithm, which propagates and selects among input values using min and max operations whenever a root reaches the network.

If this is right

  • Stabilizing consensus is possible without any agent knowing the network size.
  • The same algorithm works when the identity of the root changes arbitrarily between periods.
  • No permanent strong connectivity or fixed spanning tree is needed.
  • Message size and local storage remain small even as the network grows.
  • The solution applies to fully anonymous agents that start without synchronized clocks or identifiers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The periodic-root condition may be close to the weakest topology assumption that still permits consensus without global knowledge.
  • Similar MinMax rules could be tested in simulations of mobile ad-hoc or sensor networks whose connectivity fluctuates.
  • The approach suggests that other agreement tasks in dynamic settings may also reduce to local min-max selections under comparable reachability conditions.

Load-bearing premise

In every sufficiently long but bounded time period there exists at least one root agent that can reach all others through the current links.

What would settle it

An execution of the algorithm on a network in which some bounded period contains no root that reaches all agents, yet all outputs still stabilize to the same input value.

Figures

Figures reproduced from arXiv: 1906.09073 by Bernadette Charron-Bost, Shlomo Moran.

Figure 1
Figure 1. Figure 1: Distributed implementation of a MinMax algorithm [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
read the original abstract

In the stabilizing consensus problem, each agent of a networked system has an input value and is repeatedly writing an output value; it is required that eventually all the output values stabilize to the same value which, moreover, must be one of the input values. We study this problem for a synchronous model with identical and anonymous agents that are connected by a time-varying topology. Our main result is a generic MinMax algorithm that solves the stabilizing consensus problem in this model when, in each sufficiently long but bounded period of time, there is an agent, called a root, that can send messages, possibly indirectly, to all the agents. Such topologies are highly dynamic (in particular, roots may change arbitrarily over time) and enforce no strong connectivity property (an agent may be never a root). Our distributed MinMax algorithms require neither central control (e.g., synchronous starts) nor any global information (eg.,on the size of the network), and are quite efficient in terms of message size and storage requirements.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper claims to introduce a generic MinMax algorithm that solves the stabilizing consensus problem for identical anonymous synchronous agents connected by a time-varying directed topology. The key enabling assumption is that in every sufficiently long but bounded time interval there exists (possibly changing) a root agent that can reach all others, possibly indirectly; the algorithm requires neither synchronous initialization nor global parameters such as network size and is claimed to be efficient in message size and local storage.

Significance. If the correctness claim holds, the result would be significant for distributed computing in dynamic networks: it supplies an explicit, parameter-free algorithm for stabilizing consensus under a weak and time-varying connectivity condition that is strictly weaker than perpetual strong connectivity, while avoiding any central control or knowledge of n.

major comments (1)
  1. [Abstract / Main result statement] The abstract asserts existence and correctness of the MinMax algorithm under the periodic-root condition, yet the provided text contains no proof sketch, invariant, or inductive argument establishing that outputs eventually stabilize to a common input value. Without the detailed analysis the central claim cannot be verified.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and for highlighting the need for explicit verification of the central correctness claim. We address the single major comment below.

read point-by-point responses
  1. Referee: [Abstract / Main result statement] The abstract asserts existence and correctness of the MinMax algorithm under the periodic-root condition, yet the provided text contains no proof sketch, invariant, or inductive argument establishing that outputs eventually stabilize to a common input value. Without the detailed analysis the central claim cannot be verified.

    Authors: The full manuscript contains a complete correctness proof (Section 4) that proceeds by exhibiting a non-increasing potential function on the multiset of output values, proving an invariant that every output remains within the convex hull of the inputs, and showing that the periodic-root condition forces all outputs to converge to the same input value in finite time. A concise proof sketch and the key invariant are also stated in the introduction. If the version seen by the referee omitted these sections, we are happy to ensure they are clearly visible; otherwise we can expand the sketch in the introduction for clarity. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained under external topology assumption

full rationale

The paper states its main result as the existence of a generic MinMax algorithm that solves stabilizing consensus precisely when the network satisfies an externally given condition: in each sufficiently long bounded interval there exists a root agent that can reach all others (possibly indirectly). This topology precondition is presented as an input assumption rather than derived from or redefined in terms of the algorithm's outputs. No self-citations, fitted parameters renamed as predictions, or ansatzes smuggled via prior work appear in the provided description. The algorithm is claimed to require neither synchronous starts nor global parameters, and correctness is asserted relative to the stated model, making the derivation independent of its own results.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The result rests on standard synchronous distributed-computing assumptions plus the domain-specific periodic-root topology condition; no free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption Synchronous communication rounds with identical anonymous agents
    Standard model assumption invoked to define the setting in which the algorithm operates.
  • domain assumption Time-varying directed graph topology contains a root in every sufficiently long bounded period
    This is the load-bearing condition stated in the abstract that enables the MinMax algorithm to guarantee stabilization.

pith-pipeline@v0.9.0 · 5696 in / 1261 out tokens · 22754 ms · 2026-05-25T18:51:06.512394+00:00 · methodology

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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages

  1. [1]

    Fis cher, and Ren´ e Peralta

    Dana Angluin, James Aspnes, Zo¨ e Diamadi, Michael J. Fis cher, and Ren´ e Peralta. Compu- tation in networks of passively mobile finite-state sensors . In PODC, pages 290–299. ACM, 2004

  2. [2]

    Fischer, and Hong Jiang

    Dana Angluin, Michael J. Fischer, and Hong Jiang. Stabil izing consensus in mobile networks. In Phillip B. Gibbons, Tarek Abdelzaher, James Aspnes, and R amesh Rao, editors, Distributed Computing in Sensor Systems , volume 4026 of Lecture Notes in Computer Science , pages 37–

  3. [3]

    Springer Berlin Heidelberg, 2006

  4. [4]

    Becchetti, A

    L. Becchetti, A. Clementi, E. Natale, F. Pasquale, and L. Trevisan. Stabilizing consensus with many opinions. In Proceedings of the Twenty-seventh Annual ACM-SIAM Symposium 2A digraph is non-split if any two nodes have a common in-neigh bor. 15 on Discrete Algorithms , SODA ’16, pages 620–635, Philadelphia, PA, USA, 2016. Soci ety for Industrial and Appli...

  5. [5]

    Agreem ent in directed dynamic networks

    Martin Biely, Peter Robinson, and Ulrich Schmid. Agreem ent in directed dynamic networks. In Guy Even and Magn´ us M. Halldorsson, editors, Proceedings of the 19th International Col- loquium on Structural Information and Communication Complexi ty (SIROCCO), volume 7355 of Lecture Notes in Computer Science , pages 73–84. Springer, Heidelberg, 2012

  6. [6]

    Blondel, Julien M

    Vincent D. Blondel, Julien M. Hendrickx, Alex Olshevsky , and John N. Tsitsiklis. Convergence in multiagent coordination, consensus, and flocking. In Proceedings of the 44th IEEE Con- ference on Decision and Control, and the European Control Confe rence (CDC-ECC) , pages 2996–3000. IEEE, New York, NY, 2005

  7. [7]

    Kroll, and Edward W

    Joseph Bonneau, Andrew Miller, Jeremy Clark, Arvind Nar ayanan, Joshua A. Kroll, and Edward W. Felten. Sok: Research perspectives and challenge s for bitcoin and cryptocurrencies. In 2015 IEEE Symposium on Security and Privacy, SP 2015, San Jose , CA, USA, May 17-21, 2015, pages 104–121, 2015

  8. [8]

    Stephen Morse, and Brian D

    Ming Cao, A. Stephen Morse, and Brian D. O. Anderson. Reac hing a consensus in a dynami- cally changing environment: a graphical approach. SIAM Journal on Control and Optimiza- tion, 47(2):575–600, 2008

  9. [9]

    Time-varying graphs and dynamic networks

    Arnaud Casteigts, Paola Flocchini, Walter Quattrocioc chi, and Nicola Santoro. Time-varying graphs and dynamic networks. In Hannes Frey, Xu Li, and Stefa n R¨ uhrup, editors,ADHOC- NOW, volume 6811 of Lecture Notes in Computer Science , pages 346–359. Springer, 2011

  10. [10]

    Approximate consensus in highly dynamic networks: The role of averaging algorithm s

    Bernadette Charron-Bost, Matthias F¨ ugger, and Thomas Nowak. Approximate consensus in highly dynamic networks: The role of averaging algorithm s. In Proceedings of the 42nd International Colloquium on Automata, Languages, and Progra mming,ICALP15, pages 528– 539, 2015

  11. [11]

    The Firing S quad Problem Revisited

    Bernadette Charron-Bost and Shlomo Moran. The Firing S quad Problem Revisited. In Rolf Niedermeier and Brigitte Vall´ ee, editors, 35th Symposium on Theoretical Aspects of Com- puter Science (STACS 2018) , volume 96 of Leibniz International Proceedings in Informatics (LIPIcs), pages 20:1–20:14, Dagstuhl, Germany, 2018. Schloss Dagst uhl–Leibniz-Zentrum fu...

  12. [12]

    The Heard -Of model: computing in distributed systems with benign faults

    Bernadette Charron-Bost and Andr´ e Schiper. The Heard -Of model: computing in distributed systems with benign faults. Distributed Computing , 22(1):49–71, 2009

  13. [13]

    A characterization o f dynamic networks where consensus is solvable

    ´Etienne Coulouma and Emmanuel Godard. A characterization o f dynamic networks where consensus is solvable. In SIROCCO, pages 24–35. Springer, 2013

  14. [14]

    Stabilizing consensus with the power of two cho ices

    Benjamin Doerr, Leslie Ann Goldberg, Lorenz Minder, Th omas Sauerwald, and Christian Scheideler. Stabilizing consensus with the power of two cho ices. In Proceedings of the Twenty- third Annual ACM Symposium on Parallelism in Algorithms and A rchitectures, SPAA ’11, pages 149–158, New York, NY, USA, 2011. ACM

  15. [15]

    Graph Algorithms

    Shimon Even. Graph Algorithms . Cambridge University Press, New York, NY, USA, 2nd edition, 2011. 16

  16. [16]

    Coordinate d consensus in dynamic networks

    Fabian Kuhn, Yoram Moses, and Rotem Oshman. Coordinate d consensus in dynamic networks. In Proceedings of the 30th ACM Symposium on Principles of Distri buted Computing (PODC) , pages 1–10. ACM, 2011

  17. [17]

    Stability of multiagent systems with time- dependent communication links

    Luc Moreau. Stability of multiagent systems with time- dependent communication links. IEEE Transactions on Automatic Control , 50(2):169–182, 2005

  18. [18]

    Bitcoin: A peer-to-peer electronic cash system

    Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system. http://bitcoin.org/bitcoin.pdf, 2008

  19. [19]

    Tsitsiklis

    Alex Olshevsky and John N. Tsitsiklis. Convergence spe ed in distributed consensus and aver- aging. SIAM Review, 53(4):747–772, 2011

  20. [20]

    Novel type of phase transition in a system of self-driven particles

    Tam´ as Vicsek, Andr´ as Czir´ ok, Eshel Ben-Jacob, InonCohen, and Ofer Shochet. Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 75(6):1226–1229, 1995

  21. [21]

    Eventually consistent

    Werner Vogels. Eventually consistent. Commun. ACM , 52(1):40–44, 2009. 17