  The control and sensing of large-scale systems results in combinatorial
problems not only for sensor and actuator placement but also for scheduling or
observability/controllability. Such combinatorial constraints in system design
and implementation can be captured using a structure known as matroids. In
particular, the algebraic structure of matroids can be exploited to develop
scalable algorithms for sensor and actuator selection, along with quantifiable
approximation bounds. However, in large-scale systems, sensors and actuators
may fail or may be (cyber-)attacked. The objective of this paper is to focus on
resilient matroid-constrained problems arising in control and sensing but in
the presence of sensor and actuator failures. In general, resilient
matroid-constrained problems are computationally hard. Contrary to the
non-resilient case (with no failures), even though they often involve objective
functions that are monotone or submodular, no scalable approximation algorithms
are known for their solution. In this paper, we provide the first algorithm,
that also has the following properties: First, it achieves system-wide
resiliency, i.e., the algorithm is valid for any number of denial-of-service
attacks or failures. Second, it is scalable, as our algorithm terminates with
the same running time as state-of-the-art algorithms for (non-resilient)
matroid-constrained optimization. Third, it provides provable approximation
bounds on the system performance, since for monotone objective functions our
algorithm guarantees a solution close to the optimal. We quantify our
algorithm's approximation performance using a notion of curvature for monotone
(not necessarily submodular) set functions. Finally, we support our theoretical
analyses with numerical experiments, by considering a control-aware sensor
selection scenario, namely, sensing-constrained robot navigation.
