Liquefied Natural Gas contracts offer cancellation options that make their pricing difficult, especially if many gas storages need to be taken into account. We develop a valuation mechanism for such contracts from the buyer’s perspective, a large gas company whose main interest in these contracts is to provide a reliable supply of gas to its clients. The approach combines valuation with hedging...

The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broad range of real-world decision problems. In order to numerically solve such programs once they get reasonably large the infinite-dimensional optimization problem has to be discretized. The stochastic optimization program generally consists of an optimization model and a stochastic model. In the m...

This paper presents a novel compressed sensing based track before detect (CS-TBD) algorithm. The proposed algorithm reconstructs the whole radar scenario (direction of arrival (DOA)Doppler plane) for each range gate at consecutive scans using an improved stagewise orthogonal matching pursuit (StOMP) algorithm, resulting in a three-dimensional range-DOA-Doppler space. It then performs temporal t...

This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamo...

This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the system’s ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of environments (scenarios). For this investigation, the paper introduces a heuristic, stagewise methodology ...

Gradient-boosted regression trees (GBRTs) have proven to be an effective solution to the learning-to-rank problem. This work proposes and evaluates techniques for training GBRTs that have efficient runtime characteristics. Our approach is based on the simple idea that compact, shallow, and balanced trees yield faster predictions: thus, it makes sense to incorporate some notion of execution cost...

An approach to obtaining a parsimonious polynomial model from time series is proposed. An optimal minimal nonuniform time series embedding schema is used to obtain a time delay kernel. This scheme recursively optimizes an objective functional that eliminates a maximum number of false nearest neighbors between successive state space reconstruction cycles. A polynomial basis is then constructed f...

Recently, Universum data that does not belong to any class of the training data, has been applied for training better classifiers. In this paper, we address a novel boosting algorithm called UadaBoost that can improve the classification performance of AdaBoost with Universum data. UadaBoost chooses a function by minimizing the loss for labeled data and Universum data. The cost function is minim...

Image deconvolution algorithms with overcomplete sparse representations and fast iterative thresholding methods are presented. The image to be recovered is assumed to be sparsely represented in a redundant dictionary of transforms. These transforms are chosen to offer a wider range of generating atoms; allowing more flexibility in image representation and adaptativity to its morphological conte...