48,920 research outputs found
Exogenous Forces in the Development of Our Banking System
A new method to optimize with orthonormal constraints is described, where a particular composition of plane (Givens) rotations is used to parameterize decision variables in terms of angles. It is showed that this parameterization is complete and that any orthonormal k-by-nmatrix can be derived to a set of no more than kn-k(k+1) angles. The technique is applied to the emph {feature extraction problem} where a linear subspace is optimized with respect to non-linear objective functions. The Optimal Discriminative Projection (ODP) algorithm is described. ODP is a data compression or feature extraction algorithm that combines powerful model optimization with regularization to avoid over training. The ODP is used primarily for classification problems
Optimising a nonlinear utility function in multi-objective integer programming
In this paper we develop an algorithm to optimise a nonlinear utility
function of multiple objectives over the integer efficient set. Our approach is
based on identifying and updating bounds on the individual objectives as well
as the optimal utility value. This is done using already known solutions,
linear programming relaxations, utility function inversion, and integer
programming. We develop a general optimisation algorithm for use with k
objectives, and we illustrate our approach using a tri-objective integer
programming problem.Comment: 11 pages, 2 tables; v3: minor revisions, to appear in Journal of
Global Optimizatio
LANDSCAPE CHANGE AND HUMAN-ENVIRONMENT INTERACTIONS: IMPLICATIONS FOR NATURAL RESOURCE MANAGEMENT IN URBANIZING AREAS
Worldwide changes in land use and land cover alter the spatial distributions of natural resources and ecosystem functions. Here I examined the pattern and process of landscape change in the Charlotte, North Carolina metropolitan region, to understand how these changes originate from and have influence on human decisions regarding land management and policy formation. First, I simulated future landscape patterns that could arise from conservation-based land use policies and assessed the potential impacts to priority natural resources and landscape composition. Second, I analyzed the process of landscape change as it originates with the decisions of individual forest owners by utilizing a unique combination of individual, site, and landscape level data within a structural equation modeling framework. Third, I used a stated preference survey to examine how those individual decisions may change with new global markets for biofuels. My findings highlight the importance of considering landscape change as a multi-scale process with integrated human, environmental, and spatial components. Advancing our understanding of these processes will support planning organizations at local to regional levels in developing sustainable natural resource management plans that are in line with societal values while preserving biodiversity and ecosystem function
Multi-parameter optimization tool for low-cost commercial fuselage crown designs
The work in progress for developing a methodology and software tool to aid in the optimal design of composite structures is discussed. The methodology is being developed to take advantage of the ability to tailor the composite material in conjunction with the design of the structure. The composites optimization design software UWCODA was found to be very successful in preliminary testing and early experience. UWCODA is a composites design code that uses a number of plies and fiber angles as design variables, employs maximum strain failure criteria for objective function and additional constraints, includes Boeing design tools for stiffened panels, and includes stiffener geometry in the design variables
Dynamic importance sampling for queueing networks
Importance sampling is a technique that is commonly used to speed up Monte
Carlo simulation of rare events. However, little is known regarding the design
of efficient importance sampling algorithms in the context of queueing
networks. The standard approach, which simulates the system using an a priori
fixed change of measure suggested by large deviation analysis, has been shown
to fail in even the simplest network setting (e.g., a two-node tandem network).
Exploiting connections between importance sampling, differential games, and
classical subsolutions of the corresponding Isaacs equation, we show how to
design and analyze simple and efficient dynamic importance sampling schemes for
general classes of networks. The models used to illustrate the approach include
-node tandem Jackson networks and a two-node network with feedback, and the
rare events studied are those of large queueing backlogs, including total
population overflow and the overflow of individual buffers.Comment: Published in at http://dx.doi.org/10.1214/105051607000000122 the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Latent class analysis for segmenting preferences of investment bonds
Market segmentation is a key component of conjoint analysis which addresses consumer
preference heterogeneity. Members in a segment are assumed to be homogenous in their
views and preferences when worthing an item but distinctly heterogenous to members of other
segments. Latent class methodology is one of the several conjoint segmentation procedures
that overcome the limitations of aggregate analysis and a-priori segmentation. The main
benefit of Latent class models is that market segment membership and regression parameters
of each derived segment are estimated simultaneously. The Latent class model presented in
this paper uses mixtures of multivariate conditional normal distributions to analyze rating
data, where the likelihood is maximized using the EM algorithm. The application focuses on
customer preferences for investment bonds described by four attributes; currency, coupon
rate, redemption term and price. A number of demographic variables are used to generate
segments that are accessible and actionable.peer-reviewe
Networks of Neuropsychological Functions in the Clinical Evaluation of Adult ADHD
This study applied network analysis to explore the relations between neuropsychological functions of individuals in the clinical evaluation of attention-deficit/hyperactivity disorder (ADHD) in adulthood. A total of 319 participants from an outpatient referral context, that is, 173 individuals with ADHD (ADHD group) and 146 individuals without ADHD (n-ADHD group), took part in this study and completed a comprehensive neuropsychological assessment. A denser network with stronger global connectivity was observed in the ADHD group compared to the n-ADHD group. The strongest connections were consistent in both networks, that is, the connections between selective attention and vigilance, and connections between processing speed, fluency, and flexibility. Further centrality estimation revealed attention-related variables to have the highest expected influence in both networks. The observed relationships between neuropsychological functions, and the high centrality of attention, may help identify neuropsychological profiles that are specific to ADHD and optimize neuropsychological assessment and treatment planning of individuals with cognitive impairment
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