A survey of nonlinear robust optimization
Robust optimization (RO) has attracted much attention from the optimization community over
the past decade. RO is dedicated to solving optimization problems subject to uncertainty …
the past decade. RO is dedicated to solving optimization problems subject to uncertainty …
Robust convex model predictive control for quadruped locomotion under uncertainties
This article considers quadruped locomotion control in the presence of uncertainties. Two
types of structured uncertainties are considered, namely, uncertain friction constraints and …
types of structured uncertainties are considered, namely, uncertain friction constraints and …
A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data
Transfer learning aims to integrate useful information from multi-source datasets to improve
the learning performance of target data. This can be effectively applied in genomics when …
the learning performance of target data. This can be effectively applied in genomics when …
Energy maximising robust control for spectral and pseudospectral methods with application to wave energy systems
D Garcia-Violini, JV Ringwood - International Journal of Control, 2021 - Taylor & Francis
Spectral and Pseudospectral methods have been widely considered in diverse optimal
control applications, usually where energy optimisation is required. Although such methods …
control applications, usually where energy optimisation is required. Although such methods …
Learning-based robust optimization: Procedures and statistical guarantees
Robust optimization (RO) is a common approach to tractably obtain safeguarding solutions
for optimization problems with uncertain constraints. In this paper, we study a statistical …
for optimization problems with uncertain constraints. In this paper, we study a statistical …
Hybrid Particle Swarm and Gray Wolf optimization for Prediction of Appliances in Low-Energy Houses
ESM El-Kenawy, MM Eid… - 2022 International …, 2022 - ieeexplore.ieee.org
In this paper, we propose a new feature optimization prediction framework. This framework
consists of two phases. The first phase is the optimization of the feature selection process, in …
consists of two phases. The first phase is the optimization of the feature selection process, in …
Oracle-based algorithms for binary two-stage robust optimization
N Kämmerling, J Kurtz - Computational Optimization and Applications, 2020 - Springer
In this work we study binary two-stage robust optimization problems with objective
uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two …
uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two …
Probabilistic guarantees in robust optimization
We develop a general methodology for deriving probabilistic guarantees for solutions of
robust optimization problems. Our analysis applies broadly to any convex compact …
robust optimization problems. Our analysis applies broadly to any convex compact …
Robust optimization for the pooling problem
The pooling problem has applications, for example, in petrochemical refining, water
networks, and supply chains and is widely studied in global optimization. To date, it has …
networks, and supply chains and is widely studied in global optimization. To date, it has …
Robust quadratic programming with mixed-integer uncertainty
A Mittal, C Gokalp… - INFORMS Journal on …, 2020 - pubsonline.informs.org
We study robust convex quadratic programs where the uncertain problem parameters can
contain both continuous and integer components. Under the natural boundedness …
contain both continuous and integer components. Under the natural boundedness …