Showing papers by "Pritam Ranjan published in 2014"

Modeling an Augmented Lagrangian for Blackbox Constrained Optimization

Abstract: Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially in addressing problems with nontrivial constraints. This situation is unfortunate because statistical methods have many attractive properties: global scope, handling noisy objectives, sensitivity analysis, and so forth. To narrow that gap, we propose a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework. This hybrid approach allows the statistical model to think globally and the augmented Lagrangian to act locally. We focus on problems where the constraints are the primary bottleneck, requiring expensive simulation to evaluate and substantial modeling effort to map out. In that context, our hybridization presents a simple yet effective solution that allows existing objective-oriented statistical approaches, like those based on Gaussian process surrogates and expected improvement heuristics, to be applied to the constrained setting with minor modification. This work is motivated by a challenging, real-data benchmark problem from hydrology where, even with a simple linear objective function, learning a nontrivial valid region complicates the search for a global minimum.

A new Bayesian ensemble of trees approach for land cover classification of satellite imagery

Abstract: Classification of satellite images is a key component of many remote sensing applications. One of the most important products of a raw satellite image is the classification that labels image pixels into meaningful classes. Though several parametric and nonparametric classifiers have been developed thus far, accurate classification still remains a challenge. In this paper, we propose a new reliable multiclass classifier for identifying class labels of a satellite image in remote sensing applications. The proposed multiclass classifier is a generalization of a binary classifier based on the flexible ensemble of regression trees model called Bayesian Additive Regression Trees. We used three small areas from the LANDSAT 5 TM image, acquired on 15 August 2009 (path–row: 08–29, L1T product, UTM map projection) over Kings County, Nova Scotia, Canada, to classify the land cover. Several prediction accuracy and uncertainty measures have been used to compare the reliability of the proposed classifier with the state...

Modeling an augmented Lagrangian for improved blackbox constrained optimization

Abstract: Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature The statistical literature is sparse, especially in addressing problems with nontrivial constraints This situation is unfortunate because statistical methods have many attractive properties: global scope, handling noisy objectives, sensitivity analysis, and so forth To narrow that gap, we propose a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework This hybrid approach allows the statistical model to think globally and the augmented Lagrangian to act locally We focus on problems where the constraints are the primary bottleneck, requiring expensive simulation to evaluate and substantial modeling effort to map out In that context, our hybridization presents a simple yet effective solution that allows existing objective-oriented statistical approaches, like those based on Gaussian process surrogates and expected improvement heuristics, to be applied to the constrained setting with minor modification This work is motivated by a challenging, real-data benchmark problem from hydrology where, even with a simple linear objective function, learning a nontrivial valid region complicates the search for a global minimum

Modeling an Augmented Lagrangian for Improved Blackbox Constrained Optimization

Abstract: Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially in addressing problems with nontrivial constraints. This situation is unfortunate because statistical methods have many attractive properties: global scope, handling noisy objectives, sensitivity analysis, and so forth. To narrow that gap, we propose a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework. This hybrid approach allows the statistical model to think globally and the augmented Lagrangian to act locally. We focus on problems where the constraints are the primary bottleneck, requiring expensive simulation to evaluate and substantial modeling effort to map out. In that context, our hybridization presents a simple yet effective solution that allows existing objective-oriented statistical approaches, like those based on Gaussian process surrogates and expected improvement heuristics, to be applied to the constrained setting with minor modification. This work is motivated by a challenging, real-data benchmark problem from hydrology where, even with a simple linear objective function, learning a nontrivial valid region complicates the search for a global minimum.