`least_squares_velocity`

Module Contents

`least_squares_velocity`(mu_grid, delta_coll_point)	We use the normal equations to solve for the derivative approximations, skipping the assembly of the original matrices
`least_squares_velocity_old`(mu_grid, delta_coll_point)	We use the normal equations to solve for the derivative approximations, skipping the assembly of the original matrices
`unstructured_least_squares_velocity`(mu, ...[, ...])
`unstructured_least_squares_velocity_old`(mu, ...)
`unstructured_least_squares_velocity_tri`(mu, ...[, ...])

least_squares_velocity.least_squares_velocity(mu_grid, delta_coll_point): We use the normal equations to solve for the derivative approximations, skipping the assembly of the original matrices A^{T}Ax = A^{T}b becomes Cx = d; we generate C and d directly

least_squares_velocity.least_squares_velocity_old(mu_grid, delta_coll_point): We use the normal equations to solve for the derivative approximations, skipping the assembly of the original matrices A^{T}Ax = A^{T}b becomes Cx = d; we generate C and d directly

least_squares_velocity.unstructured_least_squares_velocity(mu, delta_coll_point, cell_adjacency, start, constant_geometry=False)

least_squares_velocity.unstructured_least_squares_velocity_old(mu, delta_coll_point, cell_adjacency)

least_squares_velocity.unstructured_least_squares_velocity_tri(mu, delta_coll_point, cell_adjacency, constant_geometry=False)