2 citations · 3 across the 8 of their papers we have counts for
10 papers
Cost-Aware Iteration Allocation for Sequential Multi-Fidelity First-Order Optimization
Amir Ardestani-Jaafari, Warren Hare, Zhongda Huang
Optimization can be expensive when evaluating the objective and its gradient requires a large dataset or a costly simulation. A cheaper approximation (i.e., a surrogate) can provid…
From Corridor Selection to Earthwork: A Multi-Stage Framework for Automated Road Design via Steiner Trees and Convex Optimization
Paavai Manimaran Vanjeenathammal, John R. J. Thompson, Warren Hare +1
Designing road networks for wind farms in complex terrain is a challenging task, especially under tight construction budgets. Traditional manual methods are time-consuming and may…
Accuracy and Relationships of Quadratic Models in Derivative-free Optimization
Yiwen Chen, Warren Hare, Lindon Roberts
We study three quadratic models in model-based derivative-free optimization: the minimum norm (MN), minimum Frobenius norm (MFN), and quadratic generalized simplex derivative (QS)…
A unified vertical alignment and earthwork model in road design with a new convex optimization model for road networks
Sayan Sadhukhan, Warren Hare, Yves Lucet
The vertical alignment optimization problem in road design seeks the optimal vertical alignment of a road at minimal cost, taking into account earthwork while meeting all safety an…
CLARSTA: A random subspace trust-region algorithm for convex-constrained derivative-free optimization
Yiwen Chen, Warren Hare, Amy Wiebe
This paper proposes a random subspace trust-region algorithm for general convex-constrained derivative-free optimization (DFO) problems. Similar to previous random subspace DFO met…
Determining inscribability of polytopes via rank minimization based on slack matrices
Yiwen Chen, João Gouveia, Warren Hare +1
A polytope is inscribable if there is a realization where all vertices lie on the sphere. In this paper, we provide a necessary and sufficient condition for a polytope to be inscri…
A general framework for floating point error analysis of simplex derivatives
Yiwen Chen, Warren Hare, Amy Wiebe
Gradient approximations are a class of numerical approximation techniques that are of central importance in numerical optimization. In derivative-free optimization, most of the gra…
The cosine measure relative to a subspace
Charles Audet, Warren Hare, Gabriel Jarry-Bolduc
The cosine measure was introduced in 2003 to quantify the richness of a finite positive spanning sets of directions in the context of derivative-free directional methods. A positiv…
-fully Quadratic Modeling and its Application in a Random Subspace Derivative-free Method
Yiwen Chen, Warren Hare, Amy Wiebe
Model-based derivative-free optimization (DFO) methods are an important class of DFO methods that are known to struggle with solving high-dimensional optimization problems. Recent…
Optimizing Damper Connectors for Adjacent Buildings
K. Bigdeli, W. Hare, J. Nutini +1
Many theoretical and experimental studies have used heuristic methods to investigate the dynamic behaviour of the passive coupling of adjacent structures. However, few papers have…