In this paper, the aim is to compute Pareto efficient solutions of multi-objective optimization problems involving forbidden regions. More precisely, we assume that the vector-valued objective function is componentwise generalized-convex and acts between a real topological linear pre-image space and a finite-dimensional image space, while the feasible set is given by the whole pre-image space … Read more
Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters. Quasi-Newton approaches based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update typically do not require manually tuning hyper-parameters but … Read more
We propose an approach based on neural networks and the AC power flow equations to identify single- and double- line outages in a power grid using the information from phasor measurement unit sensors (PMUs). Rather than inferring the outage from the sensor data by inverting the physical model, our approach uses the AC model to … Read more
We develop and present the novel minimum estimation error variance waveform design method, that optimizes the spectral shape of a unimodular radar waveform such that the performance of a joint radar-communications system that shares spectrum is maximized. We also perform a numerical study to compare the performance of the new technique with the previously derived … Read more
We study a class of quadratic stochastic programs where the distribution of random variables has unknown parameters. A traditional approach is to estimate the parameters using a maximum likelihood estimator (MLE) and to use this as input in the optimization problem. For the unconstrained case, we show that an estimator that “shrinks” the MLE towards … Read more
This paper addresses the manufacturing and distribution of short-lived radio-pharmaceuticals which are mainly used in diagnostic imaging studies. We develop a mixed integer nonlinear optimization model that is flexible enough to capture the complex underlying nuclear physics of the production process of fludeoxyglucose (FDG), which is widely used in oncology and cardiology, as well as … Read more
NeatWork is an advanced optimization and simulation tool for the design of purely gravity-driven water distribution systems aiming at delivering clean water to poor rural communities. The exclusion of any adjustable devices, such as pumps and valves, for controlling pressures and flows is motivated by two main reasons: firstly, the system should be as simple … Read more
This paper deals with the modeling of power flow in a transmission grid within the multi-sectoral multi-energy long-term regional energy model ETEM-SG. This extension of the model allows a better representation of demand response for flexible loads triggered by nodal marginal cost pricing. To keep the global model in the realm of linear program- ming … Read more
The effect of preconditioning linear weighted least-squares using an approximation of the model matrix is analyzed, showing the interplay of the eigenstructures of both the model and weighting matrices. A small example is given illustrating the resulting potential inefficiency of such preconditioners. Consequences of these results in the context of the weakly-constrained 4D-Var data assimilation … Read more
This paper discusses the practical use of the saddle variational formulation for the weakly-constrained 4D-VAR method in data assimilation. It is shown that the method, in its original form, may produce erratic results or diverge because of the inherent lack of monotonicity of the produced objective function values. Convergent, variationaly coherent variants of the algorithm … Read more