Measuring and Synthesizing Systems in Probabilistic Environments

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The sum of square roots is as follows: Given x1, . . ., xn ∈ Z and a1, . . ., an ∈ N decide whether E = Pni=1 xi √ai ≥ 0. It is a prominent open problem (Problem 33 of the Open Problems Project), whether this can be decided in polynomial time. The state-of ...

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

We define a state space and a Markov process associated to the stochastic quantisation equation of Yang-Mills-Higgs (YMH) theories. The state space S is a nonlinear metric space of distributions, elements of which can be used as initial conditions for the ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

The thesis is dedicated to the study of two main partial differential equations (PDEs) in fluid dynamics: the Navier-Stokes equations, which describe the motion of incompressible fluids, and the transport equation with divergence-free velocity fields, whic ...

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...

In this thesis, we give new approximation algorithms for some NP-hard problems arising in resource allocation and network design. As a resource allocation problem, we study the Santa Claus problem (also known as the MaxMin Fair Allocation problem) in which ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

Population equations for infinitely large networks of spiking neurons have a long tradition in theoret-ical neuroscience. In this work, we analyze a recent generalization of these equations to populations of finite size, which takes the form of a nonlinear ...

This article investigates the optimal containment control problem for a class of heterogeneous multi-agent systems with time-varying actuator faults and unmatched disturbances based on adaptive dynamic programming. Since there exist unknown input signals i ...

This paper studies the routing and charging behaviors of electric vehicles in a competitive ride-hailing market. When the vehicles are idle, they can choose whether to continue cruising to search for passengers, or move a charging station to recharge. The ...

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

Omnichannel retail has emerged as the new standard in today's commerce landscape, with retailers integrating their physical and online channels to enhance the customer shopping experience. However, such integration presents significant challenges for retai ...

We study the performance of Markov chains for the q-state ferromagnetic Potts model on random regular graphs. While the cases of the grid and the complete graph are by now well-understood, the case of random regular graphs has resisted a detailed analysis ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

In this paper, we consider the optimisation of a time varying scalar field by a network of agents with no gradient information. We propose a composite control law, blending extremum seeking with formation control in order to converge to the extrema faster ...

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference fro ...

Measuring and Synthesizing Systems in Probabilistic Environments

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An Improved Bound on Sums of Square Roots via the Subspace Theorem

A ride time-oriented scheduling algorithm for dial-a-ride problems

On distributional autoregression and iterated transportation

Stochastic quantisation of Yang-Mills-Higgs in 3D

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

Anomalous dissipation and other non-smooth phenomena in fluids

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Approximation Algorithms for Allocation and Network Design

Distributionally Robust Linear Quadratic Control

On a Finite-Size Neuronal Population Equation

Optimal containment control for a class of heterogeneous multi-agent systems with actuator faults

Routing and charging game in ride-hailing service with electric vehicles

Results on Sparse Integer Programming and Geometric Independent Sets

Quantitative Methods for Omnichannel Decision-Making

Metastability of the Potts Ferromagnet on Random Regular Graphs

Universal and adaptive methods for robust stochastic optimization

Gradient free cooperative seeking of a moving source

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Hidden Markov Modeling Over Graphs