LSQSystem Struct Reference

Inverse-distance-squared weighted least-squares normal equations M g = b, with M stored as its upper triangle (row-major). More...

#include <gradient.h>

Public Member Functions
KOKKOS_INLINE_FUNCTION void	add (const rtype *dx, const rtype *W_i, const rtype *W_j)
KOKKOS_INLINE_FUNCTION void	solve (rtype g[N_CONSERVATIVE][N_DIM]) const
	Solve for the gradient of every variable: g[i][d] = d W_i / d x_d.

Public Attributes
rtype	M [N_DIM *(N_DIM+1)/2] = {}
rtype	b [N_CONSERVATIVE][N_DIM] = {}

Detailed Description

Inverse-distance-squared weighted least-squares normal equations M g = b, with M stored as its upper triangle (row-major).

Member Function Documentation

add()

KOKKOS_INLINE_FUNCTION void LSQSystem::add ( const rtype *  dx, const rtype *  W_i, const rtype *  W_j  )

inline

solve()

KOKKOS_INLINE_FUNCTION void LSQSystem::solve ( rtype  g[N_CONSERVATIVE][N_DIM] ) const

inline

Solve for the gradient of every variable: g[i][d] = d W_i / d x_d.

Member Data Documentation

b

rtype LSQSystem::b[N_CONSERVATIVE][N_DIM] = {}

M

rtype LSQSystem::M[N_DIM *(N_DIM+1)/2] = {}

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The documentation for this struct was generated from the following file:

• /home/runner/work/mallard/mallard/mallard/src/numerics/gradient.h