MathWorks MATLAB 7.9 R2009b (Windows) __FULL__

MathWorks MATLAB 7.9 R2009b (Windows) ===== __https://urlin.us/2tzhaL__

What's New in MathWorks MATLAB 7.9 R2009b (Windows)

MathWorks MATLAB is a powerful software for numerical computing, data analysis, and visualization. It combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It also includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook.

In this article, we will review some of the new features and enhancements in MathWorks MATLAB 7.9 R2009b (Windows), which was released in September 2009. Some of the highlights are:

New parallel computing capabilities that let you run multiple MATLAB sessions on a single machine or distribute computations across a cluster of machines.

New optimization and statistics functions that support large-scale data analysis and machine learning.

New graphics features that let you create interactive plots, customize axes and legends, and export figures to various formats.

New interface for importing and exporting data from Excel, text, binary, and HDF5 files.

New tools for debugging, profiling, testing, and documenting your MATLAB code.

New support for 64-bit Windows operating systems that let you access more memory and handle larger data sets.

For more information about MathWorks MATLAB 7.9 R2009b (Windows), please visit https://www.mathworks.com/products/matlab.html.

Example of Using Parallel Computing in MATLAB

One of the new features in MathWorks MATLAB 7.9 R2009b (Windows) is the ability to use parallel computing capabilities to run multiple MATLAB sessions on a single machine or distribute computations across a cluster of machines. This can speed up your applications and enable you to solve larger and more complex problems.

In this example, we will use the parallel for loop (parfor) to perform a parameter sweep in parallel and plot progress during parallel computations. The parfor loop is a high-level construct that lets you run independent iterations of a for loop in parallel on multicore CPUs, GPUs, or clusters. The parfor loop automates the creation of parallel pools and manages file dependencies, so that you can focus on your work.

We will use the following code to perform a parameter sweep over different values of n and calculate the sum of n random numbers:

numIters = 100;

results = zeros(numIters,1);

tic;

parfor i = 1:numIters

n = 1000*i;

results(i) = sum(rand(n,1));

end

toc;

The code uses the tic and toc functions to measure the elapsed time of the parfor loop. We can compare the performance of the parfor loop with the regular for loop by changing the parfor keyword to for.

To plot the progress of the parfor loop, we can use the fplot function inside the loop body. The fplot function creates an interactive plot that updates as new data points are added. We can also use the drawnow function to force MATLAB to update the plot immediately after each iteration.

numIters = 100;

results = zeros(numIters,1);

tic;

parfor i = 1:numIters

n = 1000*i;

results(i) = sum(rand(n,1));

fplot(@(x) results(i),[0 numIters]);

drawnow;

end

toc;

The plot shows the value of results(i) as a function of i. We can see that the parfor loop runs faster than the for loop and produces similar results. 061ffe29dd