In this paper, we study risk-averse models for multicriteria optimization problems under uncertainty. We use a weighted sum-based scalarization and take a robust approach by considering a set of scalarization vectors to address the ambiguity and inconsistency in the relative weights of each criterion. We model the risk aversion of the decision makers via the concept of multivariate conditional ...

We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in practical scenarios. Our model first takes a correction step on the grossly cor...

A multivariate approach involves varying number of objectives to be satisfied simultaneously in testing process. An evolutionary approach, genetic algorithm is taken for solving multivariate problems in software engineering. The Multivariate Optimization Problem (MOP) has a set of solutions, each of which satisfies the objectives at an acceptable level. Another evolutionary algorithm named SBGA...

Abstract—A paradigm of statistical mechanics of financial markets (SMFM) is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA...

In the present study, we consider the selection of model selection criteria for multivariate ridge regression. There are several model selection criteria for selecting the ridge parameter in multivariate ridge regression, e.g., the Cp criterion and the modified Cp (MCp) criterion. We propose the generalized Cp (GCp) criterion, which includes Cp andMCp criteria as special cases. The GCp criterio...

6.2. Orthogonality versus Independence 584 6.3. Cross-sensitivity and Diversity 585 6.4. Multiple Roles of Redundancy 585 7. Data Preprocessing 586 7.1. Baseline Correction 586 7.2. Scaling 587 7.2.1. Global Techniques 588 7.2.2. Local Techniques 588 7.2.3. Nonlinear Transforms 588 8. Drift Compensation 588 8.1. Univariate Drift Compensation 589 8.2. Multivariate Drift Compensation 589 9. Featu...

Hashing has proven a valuable tool for large-scale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest—multivariate performance measures such as the AUC and NDCG. Here we present a general framework (termed Struc...

This study focused on the design optimization of permanent magnet synchronous linear motors (PMSLM) that are applied in microsecond laser cutting machines. A new design optimization method was introduced to enhance PMSLM performances in terms of motor thrust, thrust ripple, and inductive electromotive force (EMF). Based on accurate 3D finite element analysis (3D-FEA), a multiple support vector ...