arXiv subjects

Alexander Grosz

Publications and source records attributed to Alexander Grosz.

3 recordsLinked to original sources

arXiv2026

Stability in Combinatorial Markets with Side Payments

Combinatorial markets provide a general framework for trading bundles of indivisible goods. Building on the combinatorial market models of Bikhchandani & Ostroy (2002) and Bichler & Waldherr (2017), we introduce explicit side payments, thereby allowing restricted transfers of utility among subsets of agents and capturing different forms of financial collusion. This extension results in a large number of seemingly distinct market settings. However, we establish a systematic classification of their expressive power. Most notably, we show that higher-order market settings (i.e., those with more personalized prices and less clearinghouse power) are essentially equivalent in terms of market properties when side payments are permitted between buyers. An analogous collapse occurs for the third- and higher-order settings when side payments are permitted between sellers. In contrast, we identify a fundamental structural separation between the second- and third-order settings. To analyze stability in these environments, we generalize the classical concepts of stability with and without transferable utility (TU and NTU) to a partition-based notion of restricted transferability (the $T$-core). We relate stable outcomes across different settings to appropriate stability notions, identify market instances in which partially transferable utility yields a better (or any) stable outcome, and show that under personalized pricing, all stability notions collapse to NTU-stability.

cs.GT↗

arXiv2022

On the Smoothed Complexity of Combinatorial Local Search

We propose a unifying framework for smoothed analysis of combinatorial local optimization problems, and show how a diverse selection of problems within the complexity class PLS can be cast within this model. This abstraction allows us to identify key structural properties, and corresponding parameters, that determine the smoothed running time of local search dynamics. We formalize this via a black-box tool that provides concrete bounds on the expected maximum number of steps needed until local search reaches an exact local optimum. This bound is particularly strong, in the sense that it holds for any starting feasible solution, any choice of pivoting rule, and does not rely on the choice of specific noise distributions that are applied on the input, but it is parameterized by just a global upper bound $\phi$ on the probability density. The power of this tool can be demonstrated by instantiating it for various PLS-hard problems of interest to derive efficient smoothed running times (as a function of $\phi$ and the input size). Most notably, we focus on the important local optimization problem of finding pure Nash equilibria in Congestion Games, that has not been studied before from a smoothed analysis perspective. Specifically, we propose novel smoothed analysis models for general and Network Congestion Games, under various representations, including explicit, step-function, and polynomial resource latencies. We study PLS-hard instances of these problems and show that their standard local search algorithms run in polynomial smoothed time. Finally, we present further applications of our framework to a wide range of additional combinatorial problems, including local Max-Cut in weighted graphs, the Travelling Salesman problem (TSP) under the $k$-opt local heuristic, and finding pure equilibria in Network Coordination Games.

cs.CC↗

arXiv2019

Adaptive Exponential Integrators for MCTDHF

We compare exponential-type integrators for the numerical time-propagation of the equations of motion arising in the multi-configuration time-dependent Hartree-Fock method for the approximation of the high-dimensional multi-particle Schr{\"o}dinger equation. We find that among the most widely used integrators like Runge-Kutta, exponential splitting, exponential Runge-Kutta, exponential multistep and Lawson methods, exponential Lawson multistep methods with one predictor/corrector step provide optimal stability and accuracy at the least computational cost, taking into account that the evaluation of the nonlocal potential terms is by far the computationally most expensive part of such a calculation. Moreover, the predictor step provides an estimator for the time-stepping error at no additional cost, which enables adaptive time-stepping to reliably control the accuracy of a computation.

math.NA↗