ACGO

Analyzing the (k-)swap neighborhood for makespan scheduling.

Event Date: Apr 23, 2025 in ACGO, Seminars

Abstract:  Analyzing the behavior of local search methods has received considerable attention over the last two decades. One interesting question is how the simplest form of local search, i.e., iterative improvement, behaves w.r.t. a certain neighborhood both in quality of the solution as well as number of iterations needed to obtain a local optimal solution. In this talk, we consider the basic scheduling problem  in which n jobs need to be scheduled on m identical machines so as to minimize the makespan, i.e., the completion time of the last job. Finn and Horowitz (1979) showed that the...

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Sampling tree-weighted balanced graph partitions in polynomial time.

Event Date: Apr 02, 2025 in ACGO, Seminars

Abstract:  When judging the fairness of a political redistricting map, it is useful to be able to generate a large ensemble of “random” alternative maps. Formally, this is a graph partitioning problem. The objective is to output random partitions of the vertex set into k equal-sized pieces inducing connected subgraphs. Numerous algorithms have been developed to sample either exactly or approximately from the so-called “spanning tree distribution,” where each partition is weighted by the product of the numbers of spanning trees in each part. However, none of these...

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Generative Social Choice

Event Date: Apr 02, 2025 in ACGO, Seminars

The mathematical study of voting, social choice theory, has traditionally only been applicable to choices among a few predetermined alternatives, but not to open-ended decisions such as collectively selecting a textual statement. We introduce generative social choice, a design methodology for open-ended democratic processes that combines the rigor of social choice theory with the capability of large language models to generate text and extrapolate preferences. Our framework divides the design of AI-augmented democratic processes into two components: first, proving that the process satisfies...

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Truthful Budget Aggregation: Beyond Moving-Phantom Mechanisms.

Event Date: Apr 02, 2025 in ACGO, Seminars

Abstract:  We study a budget-aggregation setting in which a number of voters report their ideal distribution of a budget over a set of alternatives, and a mechanism aggregates these reports into an allocation. Ideally, such mechanisms are truthful, i.e., voters should not be incentivized to misreport their preferences. For the case of two alternatives, the set of mechanisms that are truthful and additionally meet a range of basic desiderata (anonymity, neutrality, and continuity) exactly coincides with the so-called moving-phantom mechanisms, but whether this space is richer for more...

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Two-Edge Connectivity via Pac-Man Gluing.

Event Date: Mar 19, 2025 in ACGO, Seminars

Abstract:  We study the 2-edge-connected spanning subgraph (2-ECSS) problem: Given a graph G, compute a connected subgraph H of G with the minimum number of edges such that H is spanning, i.e., V(H) = V(G), and H is 2-edge-connected, i.e., H remains connected upon the deletion of any single edge, if such an H exists. The 2-ECSS problem is known to be NP-hard. In this work, we provide a polynomial-time (5/4 + epsilon)-approximation for the problem for an arbitrarily small epsilon>0, improving the previous best approximation ratio of 13/10 + epsilon. Our improvement is based on two main...

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The Burial of Coupling Constraints in Linear Bilevel Optimization.

Event Date: Mar 12, 2025 in ACGO, Seminars

Abstract:    It has been common sense in (linear) bilevel optimization that problems with coupling constraints are more difficult to tackle than those without such constraints. While the modeling capabilities in terms of the feasible sets are indeed richer because coupling constraints allow to model disconnected feasible sets, complexity theory did not see any difference between problems with our without coupling constraints. In this talk, we show that there is no difference at all when one considers optimal solutions instead of just feasible points. For the case of optimistic bilevel...

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