Publications related to Sparse Group Covers and Greedy Tree Approximations

Efficient Sequential and Parallel Algorithms for Multistage Stochastic Integer Programming Using Proximity

Friedrich Eisenbrand, Moritz Andreas Venzin, Jana Tabea Cslovjecsek

We consider the problem of solving integer programs of the form min {c^⊺ x : Ax = b, x ∈ ℤ_{⩾ 0}}, where A is a multistage stochastic matrix in the following sense: the primal treedepth of A is bounded by a parameter d, which means that the columns of A ca ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2021

Distributionally Robust Optimization with Markovian Data

Daniel Kuhn, Mengmeng Li, Tobias Sutter

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...

2021

Bayesian Decision Making in Groups is Hard

Jan Hazla

We study the computations that Bayesian agents undertake when exchanging opinions over a network. The agents act repeatedly on their private information and take myopic actions that maximize their expected utility according to a fully rational posterior be ...

INFORMS2021

Actuator Placement under Structural Controllability using Forward and Reverse Greedy Algorithms

Maryam Kamgarpour, Tyler Summers, Baiwei Guo, Orcun Karaca

Actuator placement is an active field of research which has received significant attention for its applications in complex dynamical networks. In this paper, we study the problem of finding a set of actuator placements minimizing the metric that measures t ...

2020

New Results in Integer and Lattice Programming

Christoph Hunkenschröder

An integer program (IP) is a problem of the form

\min \{f(x) : \, Ax = b, \ l \leq x \leq u, \ x \in \Z^n\}

, where

A \in \Z^{m \times n}

,

b \in \Z^m

,

l,u \in \Z^n

, and

f: \Z^n \rightarrow \Z

is a separable convex objective function. The problem o ...

EPFL2020

MATHICSE Technical Report : Frequency-domain non-intrusive greedy Model Order Reduction based on minimal rational approximation

Fabio Nobile, Davide Pradovera

We present a technique for Model Order Reduction (MOR) of frequency-domain problems relying on rational interpolation of vector-valued functions. The selection of the sample points is carried out adaptively according to a greedy procedure. We describe seve ...

MATHICSE2020

Efficient Allocations in Constant Time: Towards Scalable Solutions in the Era of Large Scale Intelligent Systems

Boi Faltings, Panayiotis Danassis

The next technological revolution will be interwoven to the proliferation of intelligent systems. As we bridge the gap between physical and cyber worlds, we will give rise to large-scale, multi-agent based technologies. A key challenge that cities of the f ...

IOS PRESS2020

A Reinforcement Learning Approach to Train Routing and Scheduling

Zora Oswald

Good train scheduling for a big network with many trains is very hard to achieve. As the trains are competing for the tracks with one another, the number of constraints grows rapidly. Trying to take advantage of emerging technologies in the areas of optimi ...

2020

A note on the integrality gap of the configuration LP for restricted Santa Claus

Klaus Jansen, Lars Rohwedder

In the restricted Santa Claus problem we are given resources R and players P. Every resource j is an element of R. has a value v(j) and every player i is an element of P desires a set of resources R(i). We are interested in distributing the resources to pl ...

ELSEVIER2020

Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples

Mikhail Kapralov, Slobodan Mitrovic, Ashkan Norouzi Fard, Jakab Tardos

Given a source of iid samples of edges of an input graph G with n vertices and m edges, how many samples does one need to compute a constant factor approximation to the maximum matching size in G? Moreover, is it possible to obtain such an estimate in a sm ...

ASSOC COMPUTING MACHINERY2020

Sparse Group Covers and Greedy Tree Approximations

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Efficient Sequential and Parallel Algorithms for Multistage Stochastic Integer Programming Using Proximity

Distributionally Robust Optimization with Markovian Data

Bayesian Decision Making in Groups is Hard

Actuator Placement under Structural Controllability using Forward and Reverse Greedy Algorithms

New Results in Integer and Lattice Programming

MATHICSE Technical Report : Frequency-domain non-intrusive greedy Model Order Reduction based on minimal rational approximation

Efficient Allocations in Constant Time: Towards Scalable Solutions in the Era of Large Scale Intelligent Systems

A Reinforcement Learning Approach to Train Routing and Scheduling

A note on the integrality gap of the configuration LP for restricted Santa Claus

Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples

Efficient Sequential and Parallel Algorithms for Multistage Stochastic Integer Programming Using Proximity

Distributionally Robust Optimization with Markovian Data

Bayesian Decision Making in Groups is Hard

New Results in Integer and Lattice Programming

A Reinforcement Learning Approach to Train Routing and Scheduling

Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples

Actuator Placement under Structural Controllability using Forward and Reverse Greedy Algorithms

MATHICSE Technical Report : Frequency-domain non-intrusive greedy Model Order Reduction based on minimal rational approximation

A note on the integrality gap of the configuration LP for restricted Santa Claus

Efficient Allocations in Constant Time: Towards Scalable Solutions in the Era of Large Scale Intelligent Systems