scholarly journals Computing moment inequality models using constrained optimization

2021 ◽  
Author(s):  
Baiyu Dong ◽  
Yu-Wei Hsieh ◽  
Matthew Shum

Abstract Inference for moment inequality models is computationally demanding and often involves time-consuming grid search. By exploiting the equivalent formulations between unconstrained and constrained optimization, we establish new ways to compute the identified set and its confidence set in moment inequality models which overcome some of these computational hurdles. In simulations, using both linear and nonlinear moment inequality models, we show that our method significantly improves the solution quality and save considerable computing resources relative to conventional grid search. Our methods are user-friendly and can be implemented using a variety of canned software packages.

Proceedings ◽  
2020 ◽  
Vol 62 (1) ◽  
pp. 9
Author(s):  
Oriol Vallcorba ◽  
Jordi Rius

The d1Dplot and d2Dplot computer programs have been developed as user-friendly tools for the inspection and processing of 1D and 2D X-ray diffraction (XRD) data, respectively. d1Dplot provides general tools for data processing and includes the ability to generate comprehensive 2D plots of multiple patterns to easily follow transformation processes. d2Dplot is a full package for 2D XRD data. Besides general processing tools, it includes specific data analysis routines for the application of the through-the-substrate methodology [Rius et al. IUCrJ 2015, 2, 452–463]. Both programs allow the creation of a user compound database for the identification of crystalline phases. The software can be downloaded from the ALBA Synchrotron Light Source website and can be used free of charge for non-commercial and academic purposes.


2019 ◽  
Vol 36 (5) ◽  
pp. 1647-1648 ◽  
Author(s):  
Bilal Wajid ◽  
Hasan Iqbal ◽  
Momina Jamil ◽  
Hafsa Rafique ◽  
Faria Anwar

Abstract Motivation Metabolomics is a data analysis and interpretation field aiming to study functions of small molecules within the organism. Consequently Metabolomics requires researchers in life sciences to be comfortable in downloading, installing and scripting of software that are mostly not user friendly and lack basic GUIs. As the researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines truly speeding up the learning curve to build software workstations. Therefore, this paper aims to provide MetumpX, a software package that eases in the installation of 103 software by automatically resolving their individual dependencies and also allowing the users to choose which software works best for them. Results MetumpX is a Ubuntu-based software package that facilitate easy download and installation of 103 tools spread across the standard metabolomics pipeline. As far as the authors know MetumpX is the only solution of its kind where the focus lies on automating development of software workstations. Availability and implementation https://github.com/hasaniqbal777/MetumpX-bin. Supplementary information Supplementary data are available at Bioinformatics online.


2016 ◽  
Vol 14 (05) ◽  
pp. 1750048
Author(s):  
Lin-Feng Chen ◽  
Xu-Qu Hu

A Goal-Oriented and Model-Constrained Optimization (GOMCO) approach was proposed as a discriminant technique for the Variational Geomano Method (VGM) in the coefficient determinations of variational multiscale Unresolved-Scale (URS) model in steady Stokes equations. Numerical implementations using both linear and nonlinear models were performed with both the GOMCO and VGM. Numerical results show that the coefficients determined by the GOMCO are scale-invariant, while they are scale-variant by the VGM. The GOMCO technique is found to be more appropriate for coefficient determinations in steady Stokes equations, as the VGM is sensitive to the computing procedures. Moreover, the GOMCO could provide reliable coefficients for the URS model.


2005 ◽  
Vol 13 (3) ◽  
pp. 329-352 ◽  
Author(s):  
Lauren Clevenger ◽  
Lauren Ferguson ◽  
William E. Hart

We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.


2021 ◽  
Vol 8 (2) ◽  
pp. 201424
Author(s):  
Dan Cao ◽  
Yuan Chen ◽  
Jin Chen ◽  
Hongyan Zhang ◽  
Zheming Yuan

The maximal information coefficient (MIC) captures both linear and nonlinear correlations between variable pairs. In this paper, we proposed the BackMIC algorithm for MIC estimation. The BackMIC algorithm adds a searching back process on the equipartitioned axis to obtain a better grid partition than the original implementation algorithm ApproxMaxMI. And similar to the ChiMIC algorithm, it terminates the grid search process by the χ 2 -test instead of the maximum number of bins B( n , α ). Results on simulated data show that the BackMIC algorithm maintains the generality of MIC, and gives more reasonable grid partition and MIC values for independent and dependent variable pairs under comparable running times. Moreover, it is robust under different α in B( n , α ). MIC calculated by the BackMIC algorithm reveals an improvement in statistical power and equitability. We applied (1-MIC) as the distance measurement in the K-means algorithm to perform a clustering of the cancer/normal samples. The results on four cancer datasets demonstrated that the MIC values calculated by the BackMIC algorithm can obtain better clustering results, indicating the correlations between samples measured by the BackMIC algorithm were more credible than those measured by other algorithms.


2011 ◽  
Vol 4 (8) ◽  
pp. 19
Author(s):  
Paul F. Schikora ◽  
Brian D. Neureuther

The use of discrete event simulation as a process analysis and improvement tool is no longer limited to industrial engineering curricula. With advancements in desktop computing power, we have seen user-friendly simulation software packages become available (e.g. ProModel, Arena, ProcessModel). However, we have found it desirable that students still learn the very basic concepts behind these simulation models in order to better understand their development and use. We present a simple classroom game that teaches students the basic discrete-event simulation concepts and processes without requiring them to learn all the underlying mathematics and scientific theory.


2009 ◽  
Vol 6 (1) ◽  
Author(s):  
Wen Luo ◽  
Murali Gudipati ◽  
Kevin Jung ◽  
Mao Chen ◽  
Keith B. Marschke

SummaryDespite the large number of software tools developed to address different areas of microarray data analysis, very few offer an all-in-one solution with little learning curve. For microarray core labs, there are even fewer software packages available to help with their routine but critical tasks, such as data quality control (QC) and inventory management. We have developed a simple-to-use web portal to allow bench biologists to analyze and query complicated microarray data and related biological pathways without prior training. Both experiment-based and gene-based analysis can be easily performed, even for the first-time user, through the intuitive multi-layer design and interactive graphic links. While being friendly to inexperienced users, most parameters in Goober can be easily adjusted via drop-down menus to allow advanced users to tailor their needs and perform more complicated analysis. Moreover, we have integrated graphic pathway analysis into the website to help users examine microarray data within the relevant biological content. Goober also contains features that cover most of the common tasks in microarray core labs, such as real time array QC, data loading, array usage and inventory tracking. Overall, Goober is a complete microarray solution to help biologists instantly discover valuable information from a microarray experiment and enhance the quality and productivity of microarray core labs. The whole package is freely available at http://sourceforge.net/projects/goober. A demo web server is available at http://www.goober-array.org.


Author(s):  
Robert Niederheiser ◽  
Martin Mokroš ◽  
Julia Lange ◽  
Helene Petschko ◽  
Günther Prasicek ◽  
...  

Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives. <br><br> We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud. <br><br> While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.


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