We introduce method iR2N, a modified proximal quasi-Newton method for minimizing the sum of a \(\mathcal{C}^1\) function \(f\) and a lower semi-continuou...

This multi-model analysis examines the prospects of hydrogen in the Canadian and German energy systems, and particularly of hydrogen exports from Canada to...

In this paper, a new deterministic model is developed for the real-time optimization of microgrids with diesel gensets and battery energy storage systems. Th...

Mining complexes face increasing pressure to reduce environmental impacts, yet most short-term stochastic optimization frameworks in mining complexes negle...

This work presents a case study where four well-known derivative-free solvers are benchmarked on several instances based on the \(\textsf{solar}\) suite of...

Effective waste rock management is a crucial aspect of long-term mine planning and production scheduling. When waste management is not considered during th...

The reformulation-linearization technique (RLT) is a well-established framework for generating hierarchies of linear programming (LP) relaxations for a wide ...

We present a GPU implementation of Algorithm NCL, an augmented Lagrangian method for solving large-scale and degenerate nonlinear programs. Although interio...

We examine the abatement investment and pricing decisions within a supply chain where consumers prioritize environmental performance. The product's green r...

We investigate a \(T\)-stage dynamic network formation game with linear-quadratic payoffs. Players interact through network which they create as a result...

Dealing with climate change requires that all countries engage in costly efforts to reduce their emissions. Reaching this objective has so far been elusive...

In this paper, we propose an efficient payment scheme for sustaining cooperation in finitely many times repeated Prisoner's Dilemma games. The scheme is pa...

We characterize cooperative and non-cooperative solutions of a dynamic game played over event tree when the transition probability are not given. We assume...

Operating mining complexes constantly collect data from a wide variety of sources that directly or indirectly measure pertinent geological and geometallurg...

We develop a maximum-likelihood (ML) approach based on option markets for estimating structural models. We use dynamic programming and finite elements, der...

As the need for interpretable machine learning continues to grow, we propose a novel post-clustering method to generate counterfactual explanations for clust...

Training language models (LMs) under tight GPU memory budgets rules out standard back-propagation and motivates zeroth-order (ZO) optimization. While ZO m...

We study the integration of multi-period assignment, routing, and scheduling of care workers for home health care services. In such a context, it is importa...

Many combinatorial optimization problems, such as vehicle and crew scheduling, can be modeled using path-flow formulations, where each variable represents a ...

One-dimensional models can enable the assessment of the dynamic behavior of hydropower units during transient operation with minimal computational resources....