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20162022
most citedTuning of multivariable model predictive controllersthrough expert bandit feedback

12 citations · 25 across the 15 of their papers we have counts for

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9 papers · 1 filter

math.OC20211 cited

Zeroth-order optimisation on subsets of symmetric matrices with application to MPC tuning

Alejandro I. Maass, Chris Manzie, Iman Shames +1

This paper provides a zeroth-order optimisation framework for non-smooth and possibly non-convex cost functions with matrix parameters that are real and symmetric. We provide compl…

math.OC2021

Ordinal Optimisation and the Offline Multiple Noisy Secretary Problem

Robert Chin, Jonathan E. Rowe, Iman Shames +2

We study the success probability for a variant of the secretary problem, with noisy observations and multiple offline selection. Our formulation emulates, and is motivated by, prob…

math.OC2021

A Sequential Learning Algorithm for Probabilistically Robust Controller Tuning

Robert Chin, Chris Manzie, Iman Shames +2

We introduce a sequential learning algorithm to address a robust controller tuning problem, which in effect, finds (with high probability) a candidate solution satisfying the inter…

math.OC20201 cited

A Distributed Augmenting Path Approach for the Bottleneck Assignment Problem

Mitchell Khoo, Tony A. Wood, Chris Manzie +1

We develop an algorithm to solve the Bottleneck Assignment Problem (BAP) that is amenable to having computation distributed over a network of agents. This consists of exploring how…

math.OC2020

Exploiting Structure in the Bottleneck Assignment Problem

Mitchell Khoo, Tony A. Wood, Chris Manzie +1

An assignment problem arises when there exists a set of tasks that must be allocated to a set of agents. The bottleneck assignment problem (BAP) has the objective of minimising the…

math.OC20206 cited

Uncertainty Intervals for Robust Bottleneck Assignment

Elad Michael, Tony A. Wood, Chris Manzie +1

We examine the robustness of bottleneck assignment problems to perturbations in the assignment weights. We derive two algorithms that provide uncertainty bounds for robust assignme…