using LinearAlgebra function multiply_sparse_dense(A::SparseMatrixCSC{T}, B::AbstractMatrix{T}) where T m, n = size(A) C = zeros(T, m, size(B, 2)) for j = 1:size(B, 2) for i = 1:n for k = A.colptr[i]:(A.colptr[i+1]-1) C[A.rowval[k], j]
using SparseArrays function f(x) return x^2 + 2x + 1 end function jacobian(f, x) n = length(x) J = spzeros(n, n) for i in 1:n h = zeros(n) h[i] = 1e-8 J[:,
using DifferentialEquations using LinearAlgebra function burgers!(du, u, p, t) N = length(u) du[1] = 0.0 du[N] = 0.0 for i in 2:N-1 du[i] = -u[i]*(u[i+1] – u[i-1])/2 end end N = 100
Option 1: Using Qhull and Polyhedra Libraries To solve the issue of different volume estimates between Qhull and Polyhedra libraries in Julia, we can use both libraries to calculate the volume and
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Converting a 3D mesh to a voxelated array image can be a useful task in various applications, such as computer graphics, medical imaging, and scientific visualization. In this article, we will explore
