linear-algebra | Efficient , high-performance linear algebra for node.js

I have an array of values (concentration values), with each value taken at a different time point. I need to fit a gamma-variate curve (formula is in the picture below) to these values (i.e. find alpha and beta such that the curve best fits those points - all other variables are known.)

an example of the values i might get (crosses), and the curve I'd want to fit:

I have no idea how to do this. I tried to fit a simplified version of the formula, one that can be solved by using linear regression, by using matrices but I couldn't get it to work. That version of the formula (in which you only solve for one variable, alpha) looks like this:

simplified version, which would also be fine:

my attempt to solve fit the linear regression curve using matrices, using the vnl library (https://vxl.github.io/doc/release/core/vnl/html/index.html) looked like this. I was following this guy's tutorial (https://machinelearningmastery.com/solve-linear-regression-using-linear-algebra/)

• Problem Summary

There are two .gif images in my blog post, which are breaking the responsiveness of my site, they don't seem to get resized when opened on a mobile device. Although they seem to be fine when opened from pc.

PC view:

Mobile view:

As you can see, in mobile view the two .gif images are still the same size, which breaks the responsiveness of the page. Is there a way I could solve this issue?

• The syntax I've used to include the .gif in my .mdx file is-

  ![otter dancing with a fish](./neural_net_data_manupulation_2.gif)

• Config.js file of my site:

Trying to make sense of cusolverDnDSgels function. If I run it with simple 3x3 example as in the docs it works, but when I run it with my data then d_info returns -1 which as the docs says if d_info = -i then i-th argument is not valid.

Bellow I posted the code with 3 by 3 and 4 by 3 matrices where the former works and second doesn't.

As a reference I used this web site calculator https://adrianstoll.com/linear-algebra/least-squares.html

I want to write applications in JavaScript that require a large amount of numerical computation. However, I'm very confused about the state of efficient linear-algebra-like computation in client-side JavaScript. There seems to be many approaches, but no clear indication of their readiness. Most of them seem to have restrictions of the size of vectors and matrices allowed for computation.

Obviously allows for vector and matrix computations on the GPU, but I'm not clear on the limitations. Attempted wrappers around this library seem to limit size of matrices and vectors. Is this a practical limitation (browsers don't support anything else) or just a development limitation (someone need to write the code)?

WebCL is a proposed browser-level implementation of OpenCL, but appears to be stuck in development.

Apple has recently put forth an alternative to WebCL called WebGPU. So far, there is a prototype and demos, but it's not clear to me if this will see wide adoption.

Mozilla has put out an API for SIMD operations, but it only has experimental support.

Are vectorized computations on the browser-side supported by JavaScript?

Notes:

• My question is not "What's a good library for numerical computation in JavaScript" but "Are vectorized operations possible in JavaScript?" An acceptable answer would link to a demo of vectorized computation working in a non-experimental browser.

• I may be getting SIMD, vectorization and GPU computation confused. I thought it was okay to use them synonymously in this context, given they all allow for efficient computations involving high-dimensional vectors using hardware acceleration.

I'm trying to blurr an image by mapping each pixel to the average of the N pixels to the right of it (in the same row). My iterative solution produces good output, but my linear-algebra solution is producing bad output.

From testing, I believe my kernel-matrix is correct; and, I know the last N rows don't get blurred, but that's fine for now. I'd appreciate any hints or solutions.

iterative-solution output (good), linear-algebra output (bad)

original image; and here is the failing linear-algebra code:

Assuming that I'm writing a program for vector multiplication. Following the requirement in this article:

https://etrain.github.io/2015/05/28/type-safe-linear-algebra-in-scala

The multiplication should only compile successfully if the dimension of both vectors are equal. For this I define a generic type Axis that uses a shapeless literal type (the number of dimension) as the type parameter:

From the CMake Cookbook, I see that we can use the command add_custom_command and add_custom_target to run a custom command at build time. There is a toy example that I want to extract compressed files in subdirectory and link it to the final executable files. We have two CMakeLists.txt files and the following one is in the subdirectory.

Using the chapel code inside online IDE for prototyping, an example2.chpl from Documentation about how to use the LinearAlgebra module proc eig(…) failed to operate on CSR-sparse matrix of complex values, csrMatrixA-instance.

Code:

I'm new to Julia and this seems like a straight-forward operation but for some reason I am not finding the answer anywhere.

I have been going through some tutorials online and they simply use exp(A) where A is a nxm matrix but this gives me a DimensionMismatch error.

I looked through the documentation on the official website in the elementary functions as well as the linear algebra section and googled it multiple times but can't find it for the life of me.