Let \(G=(V,E)\) be a graph and let \(S\subseteq V\) be a subset of its vertices. If the subgraph of \(G\) induced by \(V\setminus S\) is acyclic, the...

Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...

The determinantal point process (DPP) provides a promising and attractive alternative to simple random sampling in cluster analysis or classification, for th...

The crew pairing problem is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of...

With sufficient layers, enough training data, enough time, and often a custom tailored architecture, modern deep learning methods can be extremely successf...

Deploying applications having many processes in a multi-cloud environment is challenging. The cloud manager has goals such as minimizing power consumption, w...

Clustering algorithms help identify homogeneous subgroups from data. In some cases, additional information about the relationship among some subsets of the d...

A highly influential ingredient of many techniques designed to exploit sparsity in numerical optimization is the so-called chordal extension of a graph repre...

We introduce the conditional \(p\)-dispersion problem (c-pDP), an incremental variant of the \(p\)-dispersion problem (pDP). In the c-pDP, one is given a...

In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood fun...

Significant progress has been made in the field of computer vision, due to the development of supervised machine learning algorithms, which efficiently extra...

The deepening penetration of renewable power generation is challenging how the minute balancing of supply and demand is carried out by power system operators...

We consider three colouring problems which are variations of the basic vertex-colouring problem, and are motivated by applications from various domains. We g...

Reducing the number of features used in data classification can remove noisy or redundant features, reduce the cost of data collection, and improve the accur...

This paper studies the Dynamic Facility Location Problem with Modular Capacities (DFLPM). We propose a linear relaxation based heuristic (LRH) and an evoluti...

\(K\)-medoids clustering is among the most popular methods for cluster analysis, but it carries several assumptions about the nature of the latent clusters...

We present a case study of using machine learning classification algorithms to initialize a large scale commercial operations research solver (GENCOL) in the...

Drawing on statistical learning theory, we derive out-of-sample and optimality guarantees about the investment strategy obtained from a regularized portfoli...

We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each...

For nearest neighbor univariate random walks in a periodic environment, where the probability of moving depends on a periodic function, we show how to estim...