Neural network pruning and quantization techniques are almost as old as neural networks
themselves. However, to date, only ad-hoc comparisons between the two have been …

Traditional symbol detection algorithms either perform poorly or are impractical to implement
for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning …

Model predictive control (MPC) facilitates online optimal resource scheduling in electrical
networks, thermal systems, water networks, process industry to name a few. In electrical …

In the late 19th century, Swedish mathematician Edvard Phragmén proposed a load-
balancing approach for selecting committees based on approval ballots. We consider three …

This letter presents a resilient mechanism to allocate heterogeneous robots to tasks under
difficult environmental conditions such as weather events or adversarial attacks. Our primary …

Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be
an NP-hard problem. Conventional heuristic algorithms are either too complex to be …

In hybrid model predictive control (MPC), a mixed-integer quadratic program (MIQP) is
solved at each sampling time to compute the optimal control action. Although these …

Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO)
systems is known to be NP-hard. Hence, the objective of any detector of practical relevance …

The first quantum computers are expected to perform well on quadratic optimisation
problems. In this paper a quadratic problem in finance is taken, the Portfolio Optimisation …

We develop a real-time feasible mixed-integer programming-based decision-making (MIP-
DM) system for automated driving (AD). Using a linear vehicle model in a road-aligned …