Matlab has a reputation for running slowly. Here are some pointers on how to speed computations, to an often unexpected degree. Subjects currently covered: Matrix Coding Implicit Multithreading on a Multicore Machine Sparse Matrices Sub-Block Computation to Avoid Memory Overflow Matrix Coding - 1 Matlab documentation notes that efficient computation depends on using the matrix facilities, and that mathematically identical algorithms can have very different runtimes, but they are a bit coy about just what these differences are. A simple but telling example: The following is the core of the GD-CLS algorithm of Berry et.al., copied from fig. 1 of Shahnaz et.al, 2006, "Document clustering using nonnegative matrix factorization': for jj = 1:maxiter A = W'*W + lambda*eye(k); for ii = 1:n b = W'*V(:,ii); H(:,ii) = A \ b; end H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end Replacing the columwise update of H with a matrix update gives: for jj = 1:maxiter A = W'*W + lambda*eye(k); B = W'*V; H = A \ B; H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end These were tested on an 8049 x 8660 sparse matrix bag of words V (.0083 non-zeros), with W of size 8049 x 50, H 50 x 8660, maxiter = 50, lambda = 0.1, and identical initial W. They were run consecutivly, multithreaded on an 8-processor Sun server, starting at ~7:30PM. Tic-toc timing was recorded. Runtimes were respectivly 6586.2 and 70.5 seconds, a 93:1 difference. The maximum absolute pairwise difference between W matrix values was 6.6e-14. Similar speedups have been consistantly observed in other cases. In one algorithm, combining matrix operations with efficient use of the sparse matrix facilities gave a 3600:1 speedup. For speed alone, C-style iterative programming should be avoided wherever possible. In addition, when a couple lines of matrix code can substitute for an entire C-style function, program clarity is much improved. Matrix Coding - 2 Applied to integration, the speed gains are not so great, largely due to the time taken to set up the and deal with the boundaries. The anyomous function setup time is neglegable. I demonstrate on a simple uniform step linearly interpolated 1-D integration of cos() from 0 to pi, which should yield zero: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)delta/2; intF = intF + fun(enda)(delta+1)/2; for ii = start+step:step:enda-step intF = intF + fun(ii); end intF = intFstep toc; intF = -2.910164109692914e-14 Elapsed time is 4.091038 seconds. Replacing the inner summation loop with the matrix equivalent speeds things up a bit: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)*step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)*delta/2; intF = intF + fun(enda)*(delta+1)/2; intF = intF + sum(fun(start+step:step:enda-step)); intF = intF*step toc; intF = -2.868419946011613e-14 Elapsed time is 0.141564 seconds. The core computation take

Matlab

## Overview

Matlab is a dataset for object detection tasks - it contains Face annotations for 220 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data set consists of Conductivity, Temperature, Depth (CTD) data in MATLAB Format from the 2002 Polar Star Mooring Cruise (AWS-02-I). These data are provided in a single mat-file (MATLAB) for the entire cruise.

Matlab code to simulate equilibrium geometry of selected cross-sections on the Lower American and Sacramento Rivers in California.

There are many MATLAB users in the Hydrology space. These scientists work as researchers and educators in academia and in agencies and institutes. Many of these institutions partner with CUAHSI and use their resources to share data and research. For data analysis and visualization, HydroShare provides integrations with Jupyter notebooks and other tools, via an ‘open with’ affordance.

MATLAB Online provides access to MATLAB from any standard web browser wherever you have internet access. It is ideal for teaching, learning and convenient, lightweight access. With MATLAB Online, you can share your scripts, live scripts, and other MATLAB files with others directly. Additionally, you can publish your scripts and live scripts to the web as PDFs or HTML and share the URL with anyone.

This web application enables the interactive exploration of MATLAB artifacts (such as Live Scripts) through a similar ‘open with’ affordance. When working with Live Scripts, users are presented with the option to open these artifacts in the Live Editor environment.

Class(matlab) Finalwork

## Overview

Class(matlab) Finalwork is a dataset for object detection tasks - it contains Fabric Defect annotations for 482 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Scripts and data acquired at the Mirror Lake Research Site, cited by the article submitted to Water Resources Research: Distributed Acoustic Sensing (DAS) as a Distributed Hydraulic Sensor in Fractured Bedrock M. W. Becker(1), T. I. Coleman(2), and C. C. Ciervo(1) 1 California State University, Long Beach, Geology Department, 1250 Bellflower Boulevard, Long Beach, California, 90840, USA. 2 Silixa LLC, 3102 W Broadway St, Suite A, Missoula MT 59808, USA. Corresponding author: Matthew W. Becker (matt.becker@csulb.edu).

The .zip file contains data from Carreon A. et. al. "Simulating Radiative Heat Transfer...", stored in MATLAB figure files (.fig extension) with file names corresponding to the figures in the paper.

The Matlab scripts will compute parametric maps from Bruker MR images as described in the JoVE paper published in 2017

In this appendix

Matlab code to ratio images

This dataset contains MATLAB .m files for replication of images and visualization of data. See READ ME file. For image processing techniques used on the raw video data from the experiments, see https://github.com/ArcGriffin/Video-Data-Processing-CASPER-/tree/main.

Download all the Matlab source code, ANSYS APDL code, and ANSYS Workbench archive project files that accompany the book in one zip file.

These Matlab/Simulink Files were used to generate the plots for the submitted paper „Mathematical Modeling and Simulation of Thyroid Homeostasis: Implications for the Allan-Herndon-Dudley-Syndrome“.

To reproduce the plots, please execute the files in the following order: 1: MM_Parameters_Healthy.m 2: MM_Healthy.slx 3: MM_Parameters_ADHS.m 4: MM_AHDS.slx 5: MM_Plot_Results and analogously regarding the linear case.

This repository contains the Matlab code and generated data for the manuscript "Data-driven geometric parameter optimization for PD-GMRES" which uses a quadtree approach to optimize parameters for the iterative solver PD-GMRES. It includes hardware specific data to allow for reproducibity of our results. Our calculations were performed using MATLAB R2019a and should be reproducible up to and including version R2022a. A change in version R2022b leads to different numerical behavior. However, the code does run on newer Matlab versions. Further information is contained in the README.

Matlab code for PSTH

This data release contains code for computing the filtering properties of cyclical infiltration in a layered vadose zone in Central Valley, California. The code for computing the filtering is described in a manuscript that is under review at Vadose Zone Journal. The updated data contained in this data release are the code to compute the filtering properties of the vadose zone. This code was developed in 2018. The code uses existing data as inputs. The inputs are from a groundwater flow model for Central Valley, California, described by Faunt et al. (2009), and from a soil texture model described by Faunt et al. (2010). The inputs from Faunt et al. (2009) and Faunt et al. (2010) were released in those years and are available for download from https://ca.water.usgs.gov/projects/central-valley/central-valley-hydrologic-model.html

MATLAB code containing data aggregated from DRIAD output files located in "Replication Data for: DOI 10.1063/5.0075261," external datasets used for input into the present simulation, and relevant color schemes used to recreate plots used in the published paper.

Educational resources for a litter collection service-learning project integrated into a MATLAB programming course. Students collect litter data using an app, and then process that data in MATLAB to answer a variety of research questions. The project is best conducted over several weeks and is ideal for an introduction to MATLAB course. Discussions could include MATLAB concepts in addition to plastic pollution and engineering/science for social responsibility.