2 citations · 5 across the 17 of their papers we have counts for
20 papers · 1 filter
Consistency-Robustness Tradeoffs for Online Bipartite Allocation with Multiple Stages
Alexander Lindermayr, Nicole Megow, Lauren Paul
We study learning-augmented online bipartite allocation with multiple stages. In the -stage vertex-weighted fractional bipartite matching problem, demand vertices arrive in …
Better Late Than Never: Online Flow Time Scheduling with Online Estimates
Anupam Gupta, Haim Kaplan, Alexander Lindermayr +2
In the classical online flow-time scheduling problem on a single machine, jobs arrive over time and must be processed to minimize the total time they spend in the system: for over…
Online Scheduling with a Stochastic Signal
Romain Cosson, Jingwei Li, Alexander Lindermayr +1
Nonclairvoyant scheduling is a fundamental online model in which processing times are initially unknown to the scheduler. Unfortunately, for important objectives such as total comp…
The Secretary Problem with a Stochastic Precursor
Franziska Eberle, Alexander Lindermayr
In learning-augmented online algorithms, predictions are usually valued for what they say: a value estimate, a solution, or an algorithmic recommendation. This paper shows that pre…
A Simpler Analysis for -Clairvoyant Flow Time Scheduling
Anupam Gupta, Haim Kaplan, Alexander Lindermayr +2
We simplify the proof of the optimality of the Shortest Lower-Bound First (SLF) algorithm, introduced by Gupta, Kaplan, Lindermayr, Schlöter, and Yingchareonthawornchai [FOCS'25],…
Delayed-Clairvoyant Flow Time Scheduling via a Borrow Graph Analysis
Alexander Lindermayr, Jens Schlöter
We study the problem of preemptively scheduling jobs online over time on a single machine to minimize the total flow time. In the traditional clairvoyant scheduling model, the sche…
Online Flow Time Minimization with Gradually Revealed Jobs
Alexander Lindermayr, Guido Schäfer, Jens Schlöter +1
We consider the problem of online preemptive scheduling on a single machine to minimize the total flow time. In clairvoyant scheduling, where job processing times are revealed upon…
Non-Clairvoyant Scheduling with Progress Bars
Ziyad Benomar, Romain Cosson, Alexander Lindermayr +1
In non-clairvoyant scheduling, the goal is to minimize the total job completion time without prior knowledge of individual job processing times. This classical online optimization…
A Better-Than--Approximation for Two-Edge Connectivity
Felix Hommelsheim, Alexander Lindermayr, Zhenwei Liu
The 2-Edge-Connected Spanning Subgraph Problem (2ECSS) is a fundamental problem in survivable network design. Given an undirected -edge-connected graph, the goal is to find a $2…
A Little Clairvoyance Is All You Need
Anupam Gupta, Haim Kaplan, Alexander Lindermayr +2
We revisit the classical problem of minimizing the total flow time of jobs on a single machine in the online setting where jobs arrive over time. It has long been known that the Sh…
Polytope Scheduling with Groups: Unified Models and Optimal Guarantees
Alexander Lindermayr, Zhenwei Liu, Nicole Megow
We propose new abstract and unified perspectives on a range of scheduling and graph coloring problems with general min-sum objectives. Specifically, we consider various problems wh…
The Power of Proportional Fairness for Non-Clairvoyant Scheduling under Polyhedral Constraints
Sven Jäger, Alexander Lindermayr, Nicole Megow
The Polytope Scheduling Problem (PSP) was introduced by Im, Kulkarni, and Munagala (JACM 2018) as a very general abstraction of resource allocation over time and captures many well…
A -Approximation for Two-Edge Connectivity
Miguel Bosch-Calvo, Mohit Garg, Fabrizio Grandoni +3
The 2-Edge-Connected Spanning Subgraph problem (2ECSS) is among the most basic survivable network design problems: given an undirected and unweighted graph, the task is to find a s…
Two-Edge Connectivity via Pac-Man Gluing
Mohit Garg, Felix Hommelsheim, Alexander Lindermayr
We study the 2-edge-connected spanning subgraph (2-ECSS) problem: Given a graph , compute a connected subgraph of with the minimum number of edges such that is spann…
Accelerating Matroid Optimization through Fast Imprecise Oracles
Franziska Eberle, Felix Hommelsheim, Alexander Lindermayr +3
Querying complex models for precise information (e.g. traffic models, database systems, large ML models) often entails intense computations and results in long response times. Thus…
Santa Claus meets Makespan and Matroids: Algorithms and Reductions
Étienne Bamas, Alexander Lindermayr, Nicole Megow +2
In this paper we study the relation of two fundamental problems in scheduling and fair allocation: makespan minimization on unrelated parallel machines and max-min fair allocation,…
Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary
Alexander Lindermayr, Nicole Megow, Martin Rapp
We consider online scheduling on unrelated (heterogeneous) machines in a speed-oblivious setting, where an algorithm is unaware of the exact job-dependent processing speeds. We sho…
Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints
Alexandra Lassota, Alexander Lindermayr, Nicole Megow +1
We consider non-clairvoyant scheduling with online precedence constraints, where an algorithm is oblivious to any job dependencies and learns about a job only if all of its predece…
A Universal Error Measure for Input Predictions Applied to Online Graph Problems
Giulia Bernardini, Alexander Lindermayr, Alberto Marchetti-Spaccamela +3
We introduce a novel measure for quantifying the error in input predictions. The error is based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a ge…
Permutation Predictions for Non-Clairvoyant Scheduling
Alexander Lindermayr, Nicole Megow
In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a priori unknown processing requirements with the objective to minimize the total (we…