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package com.nsrddyn.fpu
import scala.math._
import scala.collection.immutable.ListSet
import scala.collection.mutable.ArrayBuffer
class CholeskyDecomposition {
/*
* Floating point operation to stress the cpu
* Calculate the number of KFLOPS / FLOPS
* implementation of the Cholesky decomposition
* More information on the Cholesky decomposition at:
* https://en.wikipedia.org/wiki/Cholesky_decomposition
*
* Linpack uses the cholesky decomposition
* https://www.netlib.org/linpack/
*
* https://www.geeksforgeeks.org/dsa/cholesky-decomposition-matrix-decomposition/
*
* The Cholesky decomposition maps matrix A into the product of A = L · LH where L is the lower triangular matrix and LH is the transposed,
* complex conjugate or Hermitian, and therefore of upper triangular form (Fig. 13.6).
* This is true because of the special case of A being a square, conjugate symmetric matrix.
*/
def run(matrix: List[List[Int]]): Unit = {
val n: Int = matrix.size
// store the lower triangular matrix
val lower = Vector[Vector[Int]]()
for (i <- 0 until n)
{
for (j <- 0 until i)
var sum: Double = 0
if j == i then
sum += math.pow(lowerBuffer(i)(j), 2)
end if
lower(i)(j) = (sqrt(matrix(i)(j))())
j += 1
}
i += 1
}
def (matrix: Vector[Vector[Int]], index: int, jindex: int ): Int = if j == 1 then return math.pow(matrix(index)(jindex)) else
}
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