Progressive construction of a parametric reduced-order model for PDE-constrained optimization

An adaptive approach to using reduced-order models as surrogates in PDE-constrained optimization is introduced that breaks the traditional offline-online framework of model order reduction. A sequence of optimization problems constrained by a given Reduced-Order Model (ROM) is defined with the goal of converging to the solution of a given PDE-constrained optimization problem. For each reduced optimization problem, the constraining ROM is trained from sampling the High-Dimensional Model (HDM) at the solution of some of the previous problems in the sequence. The reduced optimization problems are equipped with a nonlinear trust-region based on a residual error indicator to keep the optimization trajectory in a region of the parameter space where the ROM is accurate. A technique for incorporating sensitivities into a Reduced-Order Basis (ROB) is also presented, along with a methodology for computing sensitivities of the reduced-order model that minimizes the distance to the corresponding HDM sensitivity, in a suitable norm. The proposed reduced optimization framework is applied to subsonic aerodynamic shape optimization and shown to reduce the number of queries to the HDM by a factor of 4-5, compared to the optimization problem solved using only the HDM, with errors in the optimal solution far less than 0.1%

Performance Optimization Model of Stock Mutual Funds Based Financial Information System

The lack of public information on how to invest tobe a major cause of the lack of public interest on capitalmarket investment. Besides of that, for the certaininstrument such as mutual funds of society will getdifficulty in judging and choosing the mutual funds whichbe able to give optimal performance and benefit influencesthe slowness of mutual funds development.Result from this research activity is for finding themeasurement of the performance mutual funds stock byusing stage of stock rotation and stage of stock mutual fundwhich is made to be a accounting management informationsystem and financial. With this stock mutual fundperformance measurement model so that can be tool ofimportant information for investor, society about theinvestment will have good potency of financial and themajor elements causeof the performance of that mutualfunds in capital market. Therefore, society can value andchoose mutual fund which be able to give performance andoptimalization in benefit especially in capital market.Explanatory research, this research has the goal to explainthe relationship between the research variable through thehypotheses based on the data, such as: rotation stage andrisk stage concerning the stock of mutual fund performance.Multiple regression analysis will be used to test the effectof two or more independent variables on the dependentvariable . independent variable in this research is rotationstage and risk stage. Meanwhile in independent variable isthe stock mutual fund performance. The research result forthe simulation of measuring stock of mutual funds model,to measure the stock mutual funds performance

An optimization model for metabolic pathways

This article is available open access through the publisher’s website through the link below. Copyright @ The Author 2009.Motivation: Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online (http://bioinformatics.oxfordjournals.org/cgi/content/full/btp441/DC1)

A modified migration model biogeography evolutionary approach for electromagnetic device multiobjective optimization

Inthispaper, we present anefficient androbust algorithm for multiobjective optimization of electromagnetic devices.Therecentlydeveloped biogeography-based optimization (BBO) is modified byadapting its migration model function so as to improve its convergence.The proposed Modified Migration Model biogeography-based optimization (MMMBBO) algorithm is applied into the optimal geometrical design of an electromagnetic actuator. This multiobjective optimization problem is solved by maximizing the output force as well as minimizing the total weight of the actuator. The comparison between the optimization results using BBO and MMMBBO shows the superiority of the proposed approach