Matrices: Operations and Eigenvalues

Addition/Subtraction: Matrices of the same dimension can be added or subtracted element-wise.
Scalar Multiplication: Each element of the matrix is multiplied by a scalar.
Matrix Multiplication:
- Defined for matrices A (m x n) and B (n x p).
- Resulting matrix C is of size (m x p).
- Element C[i][j] is the dot product of the i-th row of A and the j-th column of B.
Transposition: Flipping a matrix over its diagonal, changing rows to columns.
Inverse: A matrix A has an inverse A⁻¹ if AA⁻¹ = A⁻¹A = I (identity matrix). Only square matrices can have inverses.

Eigenvalue: A scalar λ such that for a square matrix A, there exists a non-zero vector v (eigenvector) satisfying Av = λv.
Characteristic Polynomial: Det(A - λI) = 0, where I is the identity matrix. The roots give the eigenvalues.
Eigenvectors: Found by substituting eigenvalues back into (A - λI)v = 0 and solving for v.
Properties:
- Eigenvalues can be real or complex.
- The number of eigenvalues equals the size of the matrix.
- Eigenvectors corresponding to distinct eigenvalues are linearly independent.

Form: dy/dx = f(x, y).
Types:
- Separable: Can be expressed as g(y)dy = h(x)dx.
- Linear: Can be written as dy/dx + P(x)y = Q(x).
Solution Methods:
- Separation of Variables: Separate and integrate both sides.
- Integrating Factor: For linear equations, use μ(x) = e^(∫P(x)dx) to solve.

Form: A system of equations involving multiple interrelated functions.
Matrix Form: dx/dt = Ax, where A is a matrix of coefficients and x is a vector of variables.
Solution Methods:
- Eigenvalue Method: Solve for eigenvalues and eigenvectors of matrix A.
- Diagonalization: If A can be diagonalized, solutions can be expressed in terms of exponentials of eigenvalues.

Form: n-th order equations can be expressed as a linear combination of derivatives of a function.
Homogeneous: If the equation equals zero (e.g., y⁽ⁿ⁾ + pₙy⁽ⁿ⁻¹⁾ + ... + p₀y = 0).
Non-homogeneous: If it equals a function (e.g., y⁽ⁿ⁾ + pₙy⁽ⁿ⁻¹⁾ + ... + p₀y = g(x)).
Solution Methods:
- Characteristic Equation: For homogeneous, find roots to form general solution.
- Method of Undetermined Coefficients: For non-homogeneous, guess a particular solution.
- Variation of Parameters: Another technique for finding particular solutions.

Matrices of identical dimensions can be added or subtracted element-wise.
Scalar multiplication involves multiplying every element of the matrix by a scalar value.
Matrix multiplication requires compatible matrices, resulting in a new matrix where each element is the dot product of corresponding row and column.
The transposition of a matrix switches its rows with columns, effectively mirroring it over the main diagonal.
An inverse of a square matrix A exists if multiplying A by its inverse (A⁻¹) results in the identity matrix (I).

An eigenvalue (λ) corresponds to a non-zero vector (eigenvector) such that the equation Av = λv holds true for a square matrix A.
The characteristic polynomial, Det(A - λI) = 0, helps to find eigenvalues where I is the identity matrix.
Substituting eigenvalues back into (A - λI)v = 0 reveals the corresponding eigenvectors.
Properties of eigenvalues include the possibility of real or complex values and an equal count to the matrix size.
Eigenvectors related to different eigenvalues are linearly independent.

The general form is expressed as dy/dx = f(x, y).
Types include:
- Separable equations, restructured as g(y)dy = h(x)dx for integration.
- Linear equations, in the form dy/dx + P(x)y = Q(x).
Solution methods encompass:
- Separation of variables to isolate and integrate each side separately.
- Using an integrating factor, μ(x) = e^(∫P(x)dx), to rearrange linear equations for solving.

Systems consist of multiple interrelated equations representing functions.
Represented in matrix form as dx/dt = Ax, where A includes the coefficients and x is the variable vector.
Eigenvalue methods provide solutions for such systems by calculating matrix A's eigenvalues and eigenvectors.
Diagonalization allows for expressible solutions through exponentials of eigenvalues, if A is diagonalizable.

n-th order equations are combinations of derivatives of a function.
Classified into:
- Homogeneous equations, which equal zero, e.g., y⁽ⁿ⁾ + pₙy⁽ⁿ⁻¹⁾ +...+ p₀y = 0.
- Non-homogeneous equations, expressed as an equal function, e.g., y⁽ⁿ⁾ + pₙy⁽ⁿ⁻¹⁾ +...+ p₀y = g(x).
Solution methods include:
- The characteristic equation to find roots for general solutions in homogeneous cases.
- The method of undetermined coefficients for guessing particular solutions in non-homogeneous cases.
- Variation of parameters as an additional technique for finding particular solutions.