Category: 미분류
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Inverse Matrix Features for Eigen3 -(1) Inverse for Dense and Sparse Matrix
To compute the inverse of a matrix using Eigen, you can use the inverse() function, which is defined for both dense and sparse matrices. Here are a few examples:
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Sparse Matrix Tutorials for Eigen3
Eigen provides the SparseMatrix class for representing sparse matrices, which are matrices that have a significant number of zero elements. Here are a few examples of how you can use the SparseMatrix class in Eigen: Eigen provides many functions for performing operations on sparse matrices, such as matrix-vector multiplication and matrix-matrix multiplication. Here are some…
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4th Order Tensor Operation by Eigen3
A tensor is a multi-dimensional array, and in Eigen, the Tensor class can be used to represent a tensor of any rank (number of dimensions). Here are some examples of operations on a 4th-order tensor (tensor of rank 4) in Eigen: 1. Creating a 4th-order tensor: 2. Accessing elements of a 4th-order tensor: 3. Performing…
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Matrix Operations for Eigen3
Here are some examples of matrix operations in Eigen: 1. Summation 2. Subtraction: 3. Inner product: 4. Cross product: #Eigen3 #InnerProduct #CrossProduct #Summation of Matrix #Substration of Matrix
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Python Code for Finite Element Method for Laplace equation
Here is a general outline of the steps involved in implementing the finite element method for solving the Laplace equation in Python: Here is some sample Python code that illustrates these steps: calculate_element_stiffness_matrix(element): This function calculates the element stiffness matrix for a given element. It takes as input the element for which the stiffness matrix…
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Finite Element(FE) formulation for Laplace equation
The Laplace equation is a second-order partial differential equation that describes how a function changes over space. It is written as: ∇^2 u = 0 where ∇^2 is the Laplacian operator, and u is the function being described. To solve this equation using the finite element method, we need to discretize the problem domain into…
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Numerical Eigen Solvers
In linear algebra, an eigenvalue problem is a problem of finding the eigenvalues and eigenvectors of a matrix. Eigenvalues and eigenvectors are special values and vectors that are associated with a linear transformation, and they play an important role in many areas of mathematics and science. Numerical eigenvalue solvers are algorithms that are used to…
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About Numerical Linear Solver
Numerical linear solvers are algorithms that are used to solve systems of linear equations numerically, rather than analytically. These systems can be represented in the form Ax = b, where A is a matrix of coefficients, x is a vector of unknown variables, and b is a vector of constant terms. Some common numerical linear…
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How to compile slepc in windows environments
To compile SLEPc (Scalable Library for Eigenvalue Problem Computations) in a Windows environment, you will need to follow these steps: If you are having issues building the SLEPc library, there are a few things you can try to fix the problem:
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How to fix ModuleNotFoundError: No module named ‘_ctypes’ when you executed jupyter notebook
The error ModuleNotFoundError: No module named ‘_ctypes’ can occur when you try to run Jupyter Notebook and means that the _ctypes module, which is a part of the Python Standard Library, is not found. This can be caused by several factors, including a missing or corrupt Python installation, or a misconfigured environment. Here are some…