How to resolve the algorithm Cholesky decomposition step by step in the AutoHotkey programming language

Published on 12 May 2024 09:40 PM

How to resolve the algorithm Cholesky decomposition step by step in the AutoHotkey programming language

Table of Contents

Problem Statement

Every symmetric, positive definite matrix A can be decomposed into a product of a unique lower triangular matrix L and its transpose:

L

{\displaystyle L}

is called the Cholesky factor of

A

{\displaystyle A}

, and can be interpreted as a generalized square root of

A

{\displaystyle A}

, as described in Cholesky decomposition. In a 3x3 example, we have to solve the following system of equations: We can see that for the diagonal elements (

l

k k

{\displaystyle l_{kk}}

) of

L

{\displaystyle L}

there is a calculation pattern: or in general: For the elements below the diagonal (

l

i k

{\displaystyle l_{ik}}

, where

i

k

{\displaystyle i>k}

) there is also a calculation pattern: which can also be expressed in a general formula: Task description The task is to implement a routine which will return a lower Cholesky factor

L

{\displaystyle L}

for every given symmetric, positive definite nxn matrix

A

{\displaystyle A}

. You should then test it on the following two examples and include your output. Example 1: Example 2:

Let's start with the solution:

Step by Step solution about How to resolve the algorithm Cholesky decomposition step by step in the AutoHotkey programming language

Source code in the autohotkey programming language

Cholesky_Decomposition(A){
    L := [], n := A.Count()
    L[1,1] := Sqrt(A[1,1])
    loop % n {
        k := A_Index
        loop % n-1 {
            i := A_Index+1
            
            Sigma := 0, j := 0
            while (++j <= k-1)
                Sigma += L[i, j] * L[k, j]
            L[i, k] := (A[i, k] - Sigma) / L[k, k]
            
            Sigma := 0, j := 0
            while (++j <= k-1)
                Sigma += (L[k, j])**2    
            L[k, k] := Sqrt(A[k, k] - Sigma)
        }
    }
    loop % n{
        k := A_Index
        loop % n
            L[k, A_Index] := L[k, A_Index] ? L[k, A_Index] : 0
    }
    return L
}
ShowMatrix(L){
    for r, obj in L{
        row := ""
        for c, v in obj
            row .= Format("{:.3f}", v) ", "
        output .= "[" trim(row, ", ") "]`n,"
    }
    return "[" Trim(output, "`n,") "]"
}


A := [[25, 15, -5]
    , [15, 18,  0]
    , [-5, 0 , 11]]
L1 := Cholesky_Decomposition(A)

A := [[18, 22,  54,  42]
    , [22, 70,  86,  62]
    , [54, 86, 174, 134]
    , [42, 62, 134, 106]]
L2 := Cholesky_Decomposition(A)

MsgBox % Result := ShowMatrix(L1) "`n----`n" ShowMatrix(L2) "`n----"
return


  

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