PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 18, 2025SIAM Journal on Computing1 citationsOpen Access

Complexity and Parametric Computation of Equilibria in Atomic Splittable Congestion Games via Weighted Block Laplacians

View Full Paper
MKMax KlimmPWPhilipp Warode

Key Points

Key points are not available for this paper at this time.

Abstract

We show that computing an equilibrium in atomic splittable congestion games with player-specific affine cost functions l₄, ₈ (x) = a₄, ₈ x + b₄, ₈ is PPAD-complete. To prove that the problem is contained in PPAD, we develop a homotopy method that traces an equilibrium for varying flow demands of the players. A key technique for this method is to describe the evolution of the equilibrium locally by a novel block Laplacian matrix. Using the properties of this matrix give rise to a path following formulation for computing an equilibrium where states correspond to supports that are feasible for some demands. A closer investigation of the block Laplacian system further allows to orient the states giving rise to unique predecessor and successor states thus putting the problem into PPAD. For the PPAD-hardness, we reduce from computing an approximate equilibrium of a bimatrix win-lose game. As a byproduct of our reduction we further show that computing a multi-class Wardrop equilibrium with class dependent affine cost functions is PPAD-complete as well. As another byproduct of our PPAD-completeness proof, we obtain an algorithm that computes a continuum of equilibria parametrized by the players' flow demand. For player-specific costs, the algorithm runs in polynomial space. For games with player-independent costs, we obtain an algorithm computing all equilibria as a function of the flow demand that runs in time polynomial in the output.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Klimm et al. (2025) studied this question.

synapsesocial.com/papers/6a1d660e5b7fddc352053c63https://doi.org/10.1137/20m1361523
Ask AI
Helpful
Bookmark
Share
View Full Paper