linearalgebra | Linear algebra utilities in Kotlin | Math library

The problem is that when you do U[k, :], even though you're extracting out a row, what you get back is a column vector. So L[k+1:n,k]*U[k,:] becomes an attempt to multiply a column vector by a column vector.

One way to get a row vector aka a 1 x N Matrix in Julia (though I don't know if this is the idiomatic way) is to do U[k:k, :] instead:

The results are returned as a Matrix not a set of tuples:

your code works, try:

A[:, 1] and B[3, :] are both Vectors -- rank 1 -- so it is not automatically clear what you want here. The more likely option would be the inner product:

First, a few naming conventions that are probably not (but may be) contributing to the issues here:

• By convention, package names begin with a single capital, so I would recommend changing the name to Blueprint everywhere
• By default, ] test runs tests found in the test/runtests.jl, so I would recommend naming your top-level testing script runtests.jl to avoid confusion, even though it does seem from the errors here that test is finding your test_blueprint.jl file one way or another.

Now, while I can't test this without the full code of your package, what I suspect is happening here is the following:

• Normally, dependencies of the package you are testing (let's say MyPackage) are not required in test/Project.toml because they are implicit in MyPackage. So after a successful using MyPackage, while they will still not be available to any functions written in your test scripts (test/runtests.jl), will be available to the functions written in MyPackage -- just as if you had typed ]using MyPackage at the REPL and then run your test code there. This is the only reason you don't normally need to duplicate all the deps from the main Project.toml in test/Project.toml.
• Since the using Blueprint approach is failing here for other reasons, when you simply include the code from src/blueprint.jl, the usings within that file will in turn fail because those packages are not present in the active environment at test/Project.toml (even if they are present on your system elsewhere).
• Consequently, one quick fix to your problem with the current include("../src/blueprint.jl") approach would be to simply add those dependencies to your test/Project.toml

However, it would be more satisfying to fix the problem you are having with using Blueprint. I don't have enough information to debug this without seeing the full structure of your packages, but I would suggest as a start

• making sure that your code is properly structured as a package
• testing that, even if unregistered, you can ] add your package from the REPL by git repo URL (i.e. ] add https://some_website.com/you/Blueprint.jl)

EDIT: Upon inspection of the code linked in the comments (https://github.com/jonasseglare/Blueprint), a few other issues:

• Although they are already installed by default, standard libraries these days do need to be included in [deps]. In this case, that means the LinearAlgebra stdlib
• Any packages you are explicitly using in your test scripts, other than your package itself, do need to be added to test/Project.toml. I.e., any packages that you are directly using functions from in your test scripts (rather than just indirectly using via the exported functions of your package) do need to be included in test/Project.toml. In your case, the latter would appear to mean LinearAlgebra and FunctionalCollections, but not Setfield (that one only needs to be included in the regular Project.toml, since it's not being directly used in runtests.jl).

Consequently, with a few minor changes to your repo we are able to simply

So it looks like these two packages (Reduce and Symbolics) are using rather different formats for their symbolic expressions. Reduce is using Julia's native Expr type, the same type used to represent Julia code itself, while Symbolics is using an approach where calling a function on one or more special variable designated with @variables will return an object that records the operations performed on those variables:

The issue is that lu! does not exist for sparse matrices of type Float32. Internally, at the moment Julia internally promotes Float32 sparse matrices to Float64 anyways for solving systems. So I would recommend not working with Float32 if you want to use sparse solvers, and stay with Float64.

to make an un-initialized array of eltype Int8:

You can use prime: