Gruber et al. (2022) offered a framework how to explain "Physical time within human time", solving the 'two times problem: Here, I am asking whether such a problem exists at all. To question the question, I will appeal to neurobiological, evolutionary, and ...
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 ...
In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...
Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...
Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...
Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...
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 ...
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 ...
Time-sensitive networks, as in the context of IEEE Time-Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet), offer deterministic services with guaranteed bounded latency in order to support safety-critical applications. In this thesis, we ...
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 ...
The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...
