faer 0.24.0

//! computes the $LU$ decomposition of a given sparse matrix. see
//! [`faer::linalg::lu`](crate::linalg::lu) for more info
//!
//! the entry point in this module is [`SymbolicLu`] and
//! [`factorize_symbolic_lu`]
//!
//! # note
//! the functions in this module accept unsorted inputs, and may produce
//! unsorted decomposition factors.
use crate::assert;
use crate::internal_prelude_sp::*;
use crate::sparse::utils;
use linalg::lu::partial_pivoting::factor::PartialPivLuParams;
use linalg_sp::cholesky::simplicial::EliminationTreeRef;
use linalg_sp::{
	LuError, SupernodalThreshold, SymbolicSupernodalParams, colamd,
};
#[inline(never)]
fn resize_vec<T: Clone>(
	v: &mut alloc::vec::Vec<T>,
	n: usize,
	exact: bool,
	reserve_only: bool,
	value: T,
) -> Result<(), FaerError> {
	let reserve = if exact {
		alloc::vec::Vec::try_reserve_exact
	} else {
		alloc::vec::Vec::try_reserve
	};
	reserve(v, n.saturating_sub(v.len()))
		.map_err(|_| FaerError::OutOfMemory)?;
	if !reserve_only {
		v.resize(Ord::max(n, v.len()), value);
	}
	Ok(())
}
/// supernodal factorization module
///
/// a supernodal factorization is one that processes the elements of the $LU$
/// factors of the input matrix by blocks, rather than by single elements. this
/// is more efficient if the lu factors are somewhat dense
pub mod supernodal {
	use super::*;
	use crate::assert;

	pub fn solve_in_place_scratch<I: Index, T: ComplexField>(
		dim: usize,
		rhs_ncols: usize,
		par: Par,
	) -> StackReq {
		let _ = par;
		crate::perm::permute_rows_in_place_scratch::<I, T>(dim, rhs_ncols)
			.or(temp_mat_scratch::<T>(dim, rhs_ncols))
	}

	pub fn solve_transpose_in_place_scratch<I: Index, T: ComplexField>(
		dim: usize,
		rhs_ncols: usize,
		par: Par,
	) -> StackReq {
		solve_in_place_scratch::<I, T>(dim, rhs_ncols, par)
	}

	/// $LU$ factor structure containing the symbolic structure
	#[derive(Debug, Clone)]
	pub struct SymbolicSupernodalLu<I> {
		pub(super) supernode_ptr: alloc::vec::Vec<I>,
		pub(super) super_etree: alloc::vec::Vec<I>,
		pub(super) supernode_postorder: alloc::vec::Vec<I>,
		pub(super) supernode_postorder_inv: alloc::vec::Vec<I>,
		pub(super) descendant_count: alloc::vec::Vec<I>,
		pub(super) nrows: usize,
		pub(super) ncols: usize,
	}
	/// $LU$ factor structure containing the symbolic and numerical
	/// representations
	#[derive(Debug, Clone)]
	pub struct SupernodalLu<I, T> {
		nrows: usize,
		ncols: usize,
		nsupernodes: usize,
		supernode_ptr: alloc::vec::Vec<I>,
		l_col_ptr_for_row_idx: alloc::vec::Vec<I>,
		l_col_ptr_for_val: alloc::vec::Vec<I>,
		l_row_idx: alloc::vec::Vec<I>,
		l_val: alloc::vec::Vec<T>,
		ut_col_ptr_for_row_idx: alloc::vec::Vec<I>,
		ut_col_ptr_for_val: alloc::vec::Vec<I>,
		ut_row_idx: alloc::vec::Vec<I>,
		ut_val: alloc::vec::Vec<T>,
	}
	impl<I: Index, T> Default for SupernodalLu<I, T> {
		fn default() -> Self {
			Self::new()
		}
	}
	impl<I: Index, T> SupernodalLu<I, T> {
		/// creates a new supernodal $LU$ of a $0 \times 0$ matrix
		#[inline]
		pub fn new() -> Self {
			Self {
				nrows: 0,
				ncols: 0,
				nsupernodes: 0,
				supernode_ptr: alloc::vec::Vec::new(),
				l_col_ptr_for_row_idx: alloc::vec::Vec::new(),
				ut_col_ptr_for_row_idx: alloc::vec::Vec::new(),
				l_col_ptr_for_val: alloc::vec::Vec::new(),
				ut_col_ptr_for_val: alloc::vec::Vec::new(),
				l_row_idx: alloc::vec::Vec::new(),
				ut_row_idx: alloc::vec::Vec::new(),
				l_val: alloc::vec::Vec::new(),
				ut_val: alloc::vec::Vec::new(),
			}
		}

		/// returns the number of rows of $A$
		#[inline]
		pub fn nrows(&self) -> usize {
			self.nrows
		}

		/// returns the number of columns of $A$
		#[inline]
		pub fn ncols(&self) -> usize {
			self.ncols
		}

		/// returns the number of supernodes
		#[inline]
		pub fn n_supernodes(&self) -> usize {
			self.nsupernodes
		}

		/// solves the equation $A x = \text{rhs}$ and stores the result in
		/// `rhs`, implicitly conjugating $A$ if needed
		///
		/// # panics
		/// - panics if `self.nrows() != self.ncols()`
		/// - panics if `rhs.nrows() != self.nrows()`
		#[track_caller]
		pub fn solve_in_place_with_conj(
			&self,
			row_perm: PermRef<'_, I>,
			col_perm: PermRef<'_, I>,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			assert!(all(
				self.nrows() == self.ncols(),
				self.nrows() == rhs.nrows()
			));
			let mut X = rhs;
			crate::perm::permute_rows_in_place(X.rb_mut(), row_perm, stack);
			self.l_solve_in_place_with_conj(conj_lhs, X.rb_mut(), par, stack);
			self.u_solve_in_place_with_conj(conj_lhs, X.rb_mut(), par, stack);
			crate::perm::permute_rows_in_place(
				X.rb_mut(),
				col_perm.inverse(),
				stack,
			);
		}

		/// solves the equation $A^\top x = \text{rhs}$ and stores the result in
		/// `rhs`, implicitly conjugating $A$ if needed
		///
		/// # panics
		/// - panics if `self.nrows() != self.ncols()`
		/// - panics if `rhs.nrows() != self.nrows()`
		#[track_caller]
		pub fn solve_transpose_in_place_with_conj(
			&self,
			row_perm: PermRef<'_, I>,
			col_perm: PermRef<'_, I>,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			assert!(all(
				self.nrows() == self.ncols(),
				self.nrows() == rhs.nrows()
			));
			let mut X = rhs;
			crate::perm::permute_rows_in_place(X.rb_mut(), col_perm, stack);
			self.u_solve_transpose_in_place_with_conj(
				conj_lhs,
				X.rb_mut(),
				par,
				stack,
			);
			self.l_solve_transpose_in_place_with_conj(
				conj_lhs,
				X.rb_mut(),
				par,
				stack,
			);
			crate::perm::permute_rows_in_place(
				X.rb_mut(),
				row_perm.inverse(),
				stack,
			);
		}

		#[track_caller]
		pub(crate) fn l_solve_in_place_with_conj(
			&self,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			let lu = self;
			assert!(lu.nrows() == lu.ncols());
			assert!(lu.nrows() == rhs.nrows());
			let mut X = rhs;
			let nrhs = X.ncols();
			let supernode_ptr = &*lu.supernode_ptr;
			alloca!('stack: {
				let mut work =
					unsafe { mat![uninit::<T>, X.nrows(), X.ncols()] };
			});
			for s in 0..lu.nsupernodes {
				let s_begin = supernode_ptr[s].zx();
				let s_end = supernode_ptr[s + 1].zx();
				let s_size = s_end - s_begin;
				let s_row_idx_count = lu.l_col_ptr_for_row_idx[s + 1].zx()
					- lu.l_col_ptr_for_row_idx[s].zx();
				let L = &lu.l_val[lu.l_col_ptr_for_val[s].zx()
					..lu.l_col_ptr_for_val[s + 1].zx()];
				let L =
					MatRef::from_column_major_slice(L, s_row_idx_count, s_size);
				let (L_top, L_bot) = L.split_at_row(s_size);
				linalg::triangular_solve::solve_unit_lower_triangular_in_place_with_conj(
					L_top,
					conj_lhs,
					X.rb_mut().subrows_mut(s_begin, s_size),
					par,
				);
				linalg::matmul::matmul_with_conj(
					work.rb_mut().subrows_mut(0, s_row_idx_count - s_size),
					Accum::Replace,
					L_bot,
					conj_lhs,
					X.rb().subrows(s_begin, s_size),
					Conj::No,
					one::<T>(),
					par,
				);
				for j in 0..nrhs {
					for (idx, &i) in lu.l_row_idx[lu.l_col_ptr_for_row_idx[s]
						.zx()
						..lu.l_col_ptr_for_row_idx[s + 1].zx()][s_size..]
						.iter()
						.enumerate()
					{
						let i = i.zx();
						X[(i, j)] -= &work[(idx, j)];
					}
				}
			}
		}

		#[track_caller]
		pub(crate) fn l_solve_transpose_in_place_with_conj(
			&self,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			let lu = self;
			assert!(lu.nrows() == lu.ncols());
			assert!(lu.nrows() == rhs.nrows());
			let mut X = rhs;
			let nrhs = X.ncols();
			let supernode_ptr = &*lu.supernode_ptr;
			alloca!('stack: {
				let mut work =
					unsafe { mat![uninit::<T>, X.nrows(), X.ncols()] };
			});
			for s in (0..lu.nsupernodes).rev() {
				let s_begin = supernode_ptr[s].zx();
				let s_end = supernode_ptr[s + 1].zx();
				let s_size = s_end - s_begin;
				let s_row_idx_count = lu.l_col_ptr_for_row_idx[s + 1].zx()
					- lu.l_col_ptr_for_row_idx[s].zx();
				let L = &lu.l_val[lu.l_col_ptr_for_val[s].zx()
					..lu.l_col_ptr_for_val[s + 1].zx()];
				let L =
					MatRef::from_column_major_slice(L, s_row_idx_count, s_size);
				let (L_top, L_bot) = L.split_at_row(s_size);
				for j in 0..nrhs {
					for (idx, &i) in lu.l_row_idx[lu.l_col_ptr_for_row_idx[s]
						.zx()
						..lu.l_col_ptr_for_row_idx[s + 1].zx()][s_size..]
						.iter()
						.enumerate()
					{
						let i = i.zx();
						work[(idx, j)] = X[(i, j)].copy();
					}
				}
				linalg::matmul::matmul_with_conj(
					X.rb_mut().subrows_mut(s_begin, s_size),
					Accum::Add,
					L_bot.transpose(),
					conj_lhs,
					work.rb().subrows(0, s_row_idx_count - s_size),
					Conj::No,
					-one::<T>(),
					par,
				);
				linalg::triangular_solve::solve_unit_upper_triangular_in_place_with_conj(
					L_top.transpose(),
					conj_lhs,
					X.rb_mut().subrows_mut(s_begin, s_size),
					par,
				);
			}
		}

		#[track_caller]
		pub(crate) fn u_solve_in_place_with_conj(
			&self,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			let lu = self;
			assert!(lu.nrows() == lu.ncols());
			assert!(lu.nrows() == rhs.nrows());
			let mut X = rhs;
			let nrhs = X.ncols();
			let supernode_ptr = &*lu.supernode_ptr;
			alloca!('stack: {
				let mut work =
					unsafe { mat![uninit::<T>, X.nrows(), X.ncols()] };
			});
			for s in (0..lu.nsupernodes).rev() {
				let s_begin = supernode_ptr[s].zx();
				let s_end = supernode_ptr[s + 1].zx();
				let s_size = s_end - s_begin;
				let s_row_idx_count = lu.l_col_ptr_for_row_idx[s + 1].zx()
					- lu.l_col_ptr_for_row_idx[s].zx();
				let s_col_index_count = lu.ut_col_ptr_for_row_idx[s + 1].zx()
					- lu.ut_col_ptr_for_row_idx[s].zx();
				let L = &lu.l_val[lu.l_col_ptr_for_val[s].zx()
					..lu.l_col_ptr_for_val[s + 1].zx()];
				let L =
					MatRef::from_column_major_slice(L, s_row_idx_count, s_size);
				let U = &lu.ut_val[lu.ut_col_ptr_for_val[s].zx()
					..lu.ut_col_ptr_for_val[s + 1].zx()];
				let U_right = MatRef::from_column_major_slice(
					U,
					s_col_index_count,
					s_size,
				)
				.transpose();
				for j in 0..nrhs {
					for (idx, &i) in lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[s]
						.zx()
						..lu.ut_col_ptr_for_row_idx[s + 1].zx()]
						.iter()
						.enumerate()
					{
						let i = i.zx();
						work[(idx, j)] = X[(i, j)].copy();
					}
				}
				let (U_left, _) = L.split_at_row(s_size);
				linalg::matmul::matmul_with_conj(
					X.rb_mut().subrows_mut(s_begin, s_size),
					Accum::Add,
					U_right,
					conj_lhs,
					work.rb().subrows(0, s_col_index_count),
					Conj::No,
					-one::<T>(),
					par,
				);
				linalg::triangular_solve::solve_upper_triangular_in_place_with_conj(U_left, conj_lhs, X.rb_mut().subrows_mut(s_begin, s_size), par);
			}
		}

		#[track_caller]
		pub(crate) fn u_solve_transpose_in_place_with_conj(
			&self,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			let lu = self;
			assert!(lu.nrows() == lu.ncols());
			assert!(lu.nrows() == rhs.nrows());
			let mut X = rhs;
			let nrhs = X.ncols();
			let supernode_ptr = &*lu.supernode_ptr;
			alloca!('stack: {
				let mut work =
					unsafe { mat![uninit::<T>, X.nrows(), X.ncols()] };
			});
			for s in 0..lu.nsupernodes {
				let s_begin = supernode_ptr[s].zx();
				let s_end = supernode_ptr[s + 1].zx();
				let s_size = s_end - s_begin;
				let s_row_idx_count = lu.l_col_ptr_for_row_idx[s + 1].zx()
					- lu.l_col_ptr_for_row_idx[s].zx();
				let s_col_index_count = lu.ut_col_ptr_for_row_idx[s + 1].zx()
					- lu.ut_col_ptr_for_row_idx[s].zx();
				let L = &lu.l_val[lu.l_col_ptr_for_val[s].zx()
					..lu.l_col_ptr_for_val[s + 1].zx()];
				let L =
					MatRef::from_column_major_slice(L, s_row_idx_count, s_size);
				let U = &lu.ut_val[lu.ut_col_ptr_for_val[s].zx()
					..lu.ut_col_ptr_for_val[s + 1].zx()];
				let U_right = MatRef::from_column_major_slice(
					U,
					s_col_index_count,
					s_size,
				)
				.transpose();
				let (U_left, _) = L.split_at_row(s_size);
				linalg::triangular_solve::solve_lower_triangular_in_place_with_conj(
					U_left.transpose(),
					conj_lhs,
					X.rb_mut().subrows_mut(s_begin, s_size),
					par,
				);
				linalg::matmul::matmul_with_conj(
					work.rb_mut().subrows_mut(0, s_col_index_count),
					Accum::Replace,
					U_right.transpose(),
					conj_lhs,
					X.rb().subrows(s_begin, s_size),
					Conj::No,
					one::<T>(),
					par,
				);
				for j in 0..nrhs {
					for (idx, &i) in lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[s]
						.zx()
						..lu.ut_col_ptr_for_row_idx[s + 1].zx()]
						.iter()
						.enumerate()
					{
						let i = i.zx();
						X[(i, j)] -= &work[(idx, j)];
					}
				}
			}
		}
	}
	/// computes the layout of the workspace required to compute the symbolic
	/// $LU$ factorization of a square matrix with size `n`.
	pub fn factorize_supernodal_symbolic_lu_scratch<I: Index>(
		nrows: usize,
		ncols: usize,
	) -> StackReq {
		let _ = nrows;
		linalg_sp::cholesky::supernodal::factorize_supernodal_symbolic_cholesky_scratch::<I>(ncols)
	}
	/// computes the symbolic structure of the $LU$ factors of the matrix $A$
	#[track_caller]
	pub fn factorize_supernodal_symbolic_lu<I: Index>(
		A: SymbolicSparseColMatRef<'_, I>,
		col_perm: Option<PermRef<'_, I>>,
		min_col: &[I],
		etree: EliminationTreeRef<'_, I>,
		col_counts: &[I],
		stack: &mut MemStack,
		params: SymbolicSupernodalParams<'_>,
	) -> Result<SymbolicSupernodalLu<I>, FaerError> {
		let m = A.nrows();
		let n = A.ncols();
		with_dim!(M, m);
		with_dim!(N, n);
		let I = I::truncate;
		let A = A.as_shape(M, N);
		let min_col = Array::from_ref(
			MaybeIdx::from_slice_ref_checked(bytemuck::cast_slice(min_col), N),
			M,
		);
		let etree = etree.as_bound(N);
		let L = linalg_sp::cholesky::supernodal::ghost_factorize_supernodal_symbolic(
			A,
			col_perm.map(|perm| perm.as_shape(N)),
			Some(min_col),
			linalg_sp::cholesky::supernodal::CholeskyInput::ATA,
			etree,
			Array::from_ref(col_counts, N),
			stack,
			params,
		)?;
		let n_supernodes = L.n_supernodes();
		let mut super_etree = try_zeroed::<I>(n_supernodes)?;
		let (index_to_super, _) = unsafe { stack.make_raw::<I>(*N) };
		for s in 0..n_supernodes {
			index_to_super
				[L.supernode_begin[s].zx()..L.supernode_begin[s + 1].zx()]
				.fill(I(s));
		}
		for s in 0..n_supernodes {
			let last = L.supernode_begin[s + 1].zx() - 1;
			if let Some(parent) = etree[N.check(last)].idx() {
				super_etree[s] = index_to_super[*parent.zx()];
			} else {
				super_etree[s] = I(NONE);
			}
		}
		Ok(SymbolicSupernodalLu {
			supernode_ptr: L.supernode_begin,
			super_etree,
			supernode_postorder: L.supernode_postorder,
			supernode_postorder_inv: L.supernode_postorder_inv,
			descendant_count: L.descendant_count,
			nrows: *A.nrows(),
			ncols: *A.ncols(),
		})
	}
	struct MatU8 {
		data: alloc::vec::Vec<u8>,
		nrows: usize,
	}
	impl MatU8 {
		fn new() -> Self {
			Self {
				data: alloc::vec::Vec::new(),
				nrows: 0,
			}
		}

		fn with_dims(nrows: usize, ncols: usize) -> Result<Self, FaerError> {
			Ok(Self {
				data: try_collect((0..(nrows * ncols)).map(|_| 1u8))?,
				nrows,
			})
		}
	}
	impl core::ops::Index<(usize, usize)> for MatU8 {
		type Output = u8;

		#[inline(always)]
		fn index(&self, (row, col): (usize, usize)) -> &Self::Output {
			&self.data[row + col * self.nrows]
		}
	}
	impl core::ops::IndexMut<(usize, usize)> for MatU8 {
		#[inline(always)]
		fn index_mut(
			&mut self,
			(row, col): (usize, usize),
		) -> &mut Self::Output {
			&mut self.data[row + col * self.nrows]
		}
	}
	struct Front;
	struct LPanel;
	struct UPanel;
	#[inline(never)]
	fn noinline<T, R>(_: T, f: impl FnOnce() -> R) -> R {
		f()
	}
	/// computes the layout of the workspace required to perform a numeric $LU$
	/// factorization
	pub fn factorize_supernodal_numeric_lu_scratch<
		I: Index,
		T: ComplexField,
	>(
		symbolic: &SymbolicSupernodalLu<I>,
		params: Spec<PartialPivLuParams, T>,
	) -> StackReq {
		let m = StackReq::new::<I>(symbolic.nrows);
		let n = StackReq::new::<I>(symbolic.ncols);
		_ = params;
		StackReq::and(n, m.array(5))
	}
	/// computes the numeric values of the $LU$ factors of the matrix $A$ as
	/// well as the row pivoting permutation, and stores them in `lu` and
	/// `row_perm`/`row_perm_inv`
	pub fn factorize_supernodal_numeric_lu<I: Index, T: ComplexField>(
		row_perm: &mut [I],
		row_perm_inv: &mut [I],
		lu: &mut SupernodalLu<I, T>,
		A: SparseColMatRef<'_, I, T>,
		AT: SparseColMatRef<'_, I, T>,
		col_perm: PermRef<'_, I>,
		symbolic: &SymbolicSupernodalLu<I>,
		par: Par,
		stack: &mut MemStack,
		params: Spec<PartialPivLuParams, T>,
	) -> Result<(), LuError> {
		use linalg_sp::cholesky::supernodal::partition_fn;
		let SymbolicSupernodalLu {
			supernode_ptr,
			super_etree,
			supernode_postorder,
			supernode_postorder_inv,
			descendant_count,
			nrows: _,
			ncols: _,
		} = symbolic;
		let I = I::truncate;
		let I_checked = |x: usize| -> Result<I, FaerError> {
			if x > I::Signed::MAX.zx() {
				Err(FaerError::IndexOverflow)
			} else {
				Ok(I(x))
			}
		};
		let to_wide = |x: I| -> u128 { x.zx() as _ };
		let from_wide_checked = |x: u128| -> Result<I, FaerError> {
			if x > I::Signed::MAX.zx() as u128 {
				Err(FaerError::IndexOverflow)
			} else {
				Ok(I(x as _))
			}
		};
		let m = A.nrows();
		let n = A.ncols();
		assert!(m >= n);
		assert!(all(AT.nrows() == n, AT.ncols() == m));
		assert!(all(row_perm.len() == m, row_perm_inv.len() == m));
		let n_supernodes = super_etree.len();
		assert!(supernode_postorder.len() == n_supernodes);
		assert!(supernode_postorder_inv.len() == n_supernodes);
		assert!(supernode_ptr.len() == n_supernodes + 1);
		assert!(supernode_ptr[n_supernodes].zx() == n);
		lu.nrows = 0;
		lu.ncols = 0;
		lu.nsupernodes = 0;
		lu.supernode_ptr.clear();
		let (col_global_to_local, stack) = unsafe { stack.make_raw::<I>(n) };
		let (row_global_to_local, stack) = unsafe { stack.make_raw::<I>(m) };
		let (marked, stack) = unsafe { stack.make_raw::<I>(m) };
		let (indices, stack) = unsafe { stack.make_raw::<I>(m) };
		let (transpositions, stack) = unsafe { stack.make_raw::<I>(m) };
		let (d_active_rows, _) = unsafe { stack.make_raw::<I>(m) };
		col_global_to_local.fill(I(NONE));
		row_global_to_local.fill(I(NONE));
		marked.fill(I(0));
		resize_vec(
			&mut lu.l_col_ptr_for_row_idx,
			n_supernodes + 1,
			true,
			false,
			I(0),
		)?;
		resize_vec(
			&mut lu.ut_col_ptr_for_row_idx,
			n_supernodes + 1,
			true,
			false,
			I(0),
		)?;
		resize_vec(
			&mut lu.l_col_ptr_for_val,
			n_supernodes + 1,
			true,
			false,
			I(0),
		)?;
		resize_vec(
			&mut lu.ut_col_ptr_for_val,
			n_supernodes + 1,
			true,
			false,
			I(0),
		)?;
		lu.l_col_ptr_for_row_idx[0] = I(0);
		lu.ut_col_ptr_for_row_idx[0] = I(0);
		lu.l_col_ptr_for_val[0] = I(0);
		lu.ut_col_ptr_for_val[0] = I(0);
		for i in 0..m {
			row_perm[i] = I(i);
		}
		for i in 0..m {
			row_perm_inv[i] = I(i);
		}
		let (col_perm, col_perm_inv) = col_perm.arrays();
		let mut contrib_work = try_collect((0..n_supernodes).map(|_| {
			(
				alloc::vec::Vec::<T>::new(),
				alloc::vec::Vec::<I>::new(),
				0usize,
				MatU8::new(),
			)
		}))?;
		let work_to_mat_mut =
			|v: &mut alloc::vec::Vec<T>, nrows: usize, ncols: usize| unsafe {
				MatMut::from_raw_parts_mut(
					v.as_mut_ptr(),
					nrows,
					ncols,
					1,
					nrows as isize,
				)
			};
		let mut A_leftover = A.compute_nnz();
		for s in 0..n_supernodes {
			let s_begin = supernode_ptr[s].zx();
			let s_end = supernode_ptr[s + 1].zx();
			let s_size = s_end - s_begin;
			let s_postordered = supernode_postorder_inv[s].zx();
			let desc_count = descendant_count[s].zx();
			let mut s_row_idx_count = 0usize;
			let (left_contrib, right_contrib) = contrib_work.split_at_mut(s);
			let s_row_idxices = &mut *indices;
			for j in s_begin..s_end {
				let pj = col_perm[j].zx();
				let row_idx = A.row_idx_of_col_raw(pj);
				for i in row_idx {
					let i = i.zx();
					let pi = row_perm_inv[i].zx();
					if pi < s_begin {
						continue;
					}
					if marked[i] < I(2 * s + 1) {
						s_row_idxices[s_row_idx_count] = I(i);
						s_row_idx_count += 1;
						marked[i] = I(2 * s + 1);
					}
				}
			}
			for d in
				&supernode_postorder[s_postordered - desc_count..s_postordered]
			{
				let d = d.zx();
				let d_begin = supernode_ptr[d].zx();
				let d_end = supernode_ptr[d + 1].zx();
				let d_size = d_end - d_begin;
				let d_row_idx = &lu.l_row_idx[lu.l_col_ptr_for_row_idx[d].zx()
					..lu.l_col_ptr_for_row_idx[d + 1].zx()][d_size..];
				let d_col_ind = &lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[d].zx()
					..lu.ut_col_ptr_for_row_idx[d + 1].zx()];
				let d_col_start =
					d_col_ind.partition_point(partition_fn(s_begin));
				if d_col_start < d_col_ind.len()
					&& d_col_ind[d_col_start].zx() < s_end
				{
					for i in d_row_idx.iter() {
						let i = i.zx();
						let pi = row_perm_inv[i].zx();
						if pi < s_begin {
							continue;
						}
						if marked[i] < I(2 * s + 1) {
							s_row_idxices[s_row_idx_count] = I(i);
							s_row_idx_count += 1;
							marked[i] = I(2 * s + 1);
						}
					}
				}
			}
			lu.l_col_ptr_for_row_idx[s + 1] =
				I_checked(lu.l_col_ptr_for_row_idx[s].zx() + s_row_idx_count)?;
			lu.l_col_ptr_for_val[s + 1] = from_wide_checked(
				to_wide(lu.l_col_ptr_for_val[s])
					+ (s_row_idx_count as u128 * s_size as u128),
			)?;
			resize_vec(
				&mut lu.l_row_idx,
				lu.l_col_ptr_for_row_idx[s + 1].zx(),
				false,
				false,
				I(0),
			)?;
			resize_vec::<T>(
				&mut lu.l_val,
				lu.l_col_ptr_for_val[s + 1].zx(),
				false,
				false,
				zero::<T>(),
			)?;
			lu.l_row_idx[lu.l_col_ptr_for_row_idx[s].zx()
				..lu.l_col_ptr_for_row_idx[s + 1].zx()]
				.copy_from_slice(&s_row_idxices[..s_row_idx_count]);
			lu.l_row_idx[lu.l_col_ptr_for_row_idx[s].zx()
				..lu.l_col_ptr_for_row_idx[s + 1].zx()]
				.sort_unstable();
			let (left_row_idxices, right_row_idxices) =
				lu.l_row_idx.split_at_mut(lu.l_col_ptr_for_row_idx[s].zx());
			let s_row_idxices =
				&mut right_row_idxices[0..lu.l_col_ptr_for_row_idx[s + 1].zx()
					- lu.l_col_ptr_for_row_idx[s].zx()];
			for (idx, i) in s_row_idxices.iter().enumerate() {
				row_global_to_local[i.zx()] = I(idx);
			}
			let s_L = &mut lu.l_val[lu.l_col_ptr_for_val[s].zx()
				..lu.l_col_ptr_for_val[s + 1].zx()];
			let mut s_L = MatMut::from_column_major_slice_mut(
				s_L,
				s_row_idx_count,
				s_size,
			);
			s_L.fill(zero());
			for j in s_begin..s_end {
				let pj = col_perm[j].zx();
				let row_idx = A.row_idx_of_col(pj);
				let val = A.val_of_col(pj);
				for (i, val) in iter::zip(row_idx, val) {
					let pi = row_perm_inv[i].zx();
					if pi < s_begin {
						continue;
					}
					assert!(A_leftover > 0);
					A_leftover -= 1;
					let ix = row_global_to_local[i].zx();
					let iy = j - s_begin;
					s_L[(ix, iy)] += val;
				}
			}
			noinline(LPanel, || {
				for d in &supernode_postorder
					[s_postordered - desc_count..s_postordered]
				{
					let d = d.zx();
					if left_contrib[d].0.is_empty() {
						continue;
					}
					let d_begin = supernode_ptr[d].zx();
					let d_end = supernode_ptr[d + 1].zx();
					let d_size = d_end - d_begin;
					let d_row_idx =
						&left_row_idxices[lu.l_col_ptr_for_row_idx[d].zx()
							..lu.l_col_ptr_for_row_idx[d + 1].zx()][d_size..];
					let d_col_ind = &lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[d]
						.zx()
						..lu.ut_col_ptr_for_row_idx[d + 1].zx()];
					let d_col_start =
						d_col_ind.partition_point(partition_fn(s_begin));
					if d_col_start < d_col_ind.len()
						&& d_col_ind[d_col_start].zx() < s_end
					{
						let d_col_mid = d_col_start
							+ d_col_ind[d_col_start..]
								.partition_point(partition_fn(s_end));
						let mut d_LU_cols = work_to_mat_mut(
							&mut left_contrib[d].0,
							d_row_idx.len(),
							d_col_ind.len(),
						)
						.subcols_mut(d_col_start, d_col_mid - d_col_start);
						let left_contrib = &mut left_contrib[d];
						let d_active = &mut left_contrib.1[d_col_start..];
						let d_active_count = &mut left_contrib.2;
						let d_active_mat = &mut left_contrib.3;
						for (d_j, j) in
							d_col_ind[d_col_start..d_col_mid].iter().enumerate()
						{
							if d_active[d_j] > I(0) {
								let mut taken_rows = 0usize;
								let j = j.zx();
								let s_j = j - s_begin;
								for (d_i, i) in d_row_idx.iter().enumerate() {
									let i = i.zx();
									let pi = row_perm_inv[i].zx();
									if pi < s_begin {
										continue;
									}
									let s_i = row_global_to_local[i].zx();
									s_L[(s_i, s_j)] -= &d_LU_cols[(d_i, d_j)];
									d_LU_cols[(d_i, d_j)] = zero::<T>();
									taken_rows += d_active_mat
										[(d_i, d_j + d_col_start)]
										as usize;
									d_active_mat[(d_i, d_j + d_col_start)] = 0;
								}
								assert!(d_active[d_j] >= I(taken_rows));
								d_active[d_j] -= I(taken_rows);
								if d_active[d_j] == I(0) {
									assert!(*d_active_count > 0);
									*d_active_count -= 1;
								}
							}
						}
						if *d_active_count == 0 {
							left_contrib.0.clear();
							left_contrib.1 = alloc::vec::Vec::new();
							left_contrib.2 = 0;
							left_contrib.3 = MatU8::new();
						}
					}
				}
			});
			if s_L.nrows() < s_L.ncols() {
				return Err(LuError::SymbolicSingular {
					index: s_begin + s_L.nrows(),
				});
			}
			let transpositions = &mut transpositions[s_begin..s_end];
			crate::linalg::lu::partial_pivoting::factor::lu_in_place_recursion(
				s_L.rb_mut(),
				0,
				s_size,
				transpositions,
				par,
				params,
			);
			for (idx, t) in transpositions.iter().enumerate() {
				let i_t = s_row_idxices[idx + t.zx()].zx();
				let kk = row_perm_inv[i_t].zx();
				row_perm.swap(s_begin + idx, row_perm_inv[i_t].zx());
				row_perm_inv
					.swap(row_perm[s_begin + idx].zx(), row_perm[kk].zx());
				s_row_idxices.swap(idx, idx + t.zx());
			}
			for (idx, t) in transpositions.iter().enumerate().rev() {
				row_global_to_local.swap(
					s_row_idxices[idx].zx(),
					s_row_idxices[idx + t.zx()].zx(),
				);
			}
			for (idx, i) in s_row_idxices.iter().enumerate() {
				assert!(row_global_to_local[i.zx()] == I(idx));
			}
			let s_col_indices = &mut indices[..n];
			let mut s_col_index_count = 0usize;
			for i in s_begin..s_end {
				let pi = row_perm[i].zx();
				for j in AT.row_idx_of_col(pi) {
					let pj = col_perm_inv[j].zx();
					if pj < s_end {
						continue;
					}
					if marked[pj] < I(2 * s + 2) {
						s_col_indices[s_col_index_count] = I(pj);
						s_col_index_count += 1;
						marked[pj] = I(2 * s + 2);
					}
				}
			}
			for d in
				&supernode_postorder[s_postordered - desc_count..s_postordered]
			{
				let d = d.zx();
				let d_begin = supernode_ptr[d].zx();
				let d_end = supernode_ptr[d + 1].zx();
				let d_size = d_end - d_begin;
				let d_row_idx = &left_row_idxices[lu.l_col_ptr_for_row_idx[d]
					.zx()
					..lu.l_col_ptr_for_row_idx[d + 1].zx()][d_size..];
				let d_col_ind = &lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[d].zx()
					..lu.ut_col_ptr_for_row_idx[d + 1].zx()];
				let contributes_to_u = d_row_idx.iter().any(|&i| {
					row_perm_inv[i.zx()].zx() >= s_begin
						&& row_perm_inv[i.zx()].zx() < s_end
				});
				if contributes_to_u {
					let d_col_start =
						d_col_ind.partition_point(partition_fn(s_end));
					for j in &d_col_ind[d_col_start..] {
						let j = j.zx();
						if marked[j] < I(2 * s + 2) {
							s_col_indices[s_col_index_count] = I(j);
							s_col_index_count += 1;
							marked[j] = I(2 * s + 2);
						}
					}
				}
			}
			lu.ut_col_ptr_for_row_idx[s + 1] = I_checked(
				lu.ut_col_ptr_for_row_idx[s].zx() + s_col_index_count,
			)?;
			lu.ut_col_ptr_for_val[s + 1] = from_wide_checked(
				to_wide(lu.ut_col_ptr_for_val[s])
					+ (s_col_index_count as u128 * s_size as u128),
			)?;
			resize_vec(
				&mut lu.ut_row_idx,
				lu.ut_col_ptr_for_row_idx[s + 1].zx(),
				false,
				false,
				I(0),
			)?;
			resize_vec::<T>(
				&mut lu.ut_val,
				lu.ut_col_ptr_for_val[s + 1].zx(),
				false,
				false,
				zero::<T>(),
			)?;
			lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[s].zx()
				..lu.ut_col_ptr_for_row_idx[s + 1].zx()]
				.copy_from_slice(&s_col_indices[..s_col_index_count]);
			lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[s].zx()
				..lu.ut_col_ptr_for_row_idx[s + 1].zx()]
				.sort_unstable();
			let s_col_indices = &lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[s].zx()
				..lu.ut_col_ptr_for_row_idx[s + 1].zx()];
			for (idx, j) in s_col_indices.iter().enumerate() {
				col_global_to_local[j.zx()] = I(idx);
			}
			let s_U = &mut lu.ut_val[lu.ut_col_ptr_for_val[s].zx()
				..lu.ut_col_ptr_for_val[s + 1].zx()];
			let mut s_U = MatMut::from_column_major_slice_mut(
				s_U,
				s_col_index_count,
				s_size,
			)
			.transpose_mut();
			s_U.fill(zero());
			for i in s_begin..s_end {
				let pi = row_perm[i].zx();
				for (j, val) in
					iter::zip(AT.row_idx_of_col(pi), AT.val_of_col(pi))
				{
					let pj = col_perm_inv[j].zx();
					if pj < s_end {
						continue;
					}
					assert!(A_leftover > 0);
					A_leftover -= 1;
					let ix = i - s_begin;
					let iy = col_global_to_local[pj].zx();
					s_U[(ix, iy)] += val;
				}
			}
			noinline(UPanel, || {
				for d in &supernode_postorder
					[s_postordered - desc_count..s_postordered]
				{
					let d = d.zx();
					if left_contrib[d].0.is_empty() {
						continue;
					}
					let d_begin = supernode_ptr[d].zx();
					let d_end = supernode_ptr[d + 1].zx();
					let d_size = d_end - d_begin;
					let d_row_idx =
						&left_row_idxices[lu.l_col_ptr_for_row_idx[d].zx()
							..lu.l_col_ptr_for_row_idx[d + 1].zx()][d_size..];
					let d_col_ind = &lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[d]
						.zx()
						..lu.ut_col_ptr_for_row_idx[d + 1].zx()];
					let contributes_to_u = d_row_idx.iter().any(|&i| {
						row_perm_inv[i.zx()].zx() >= s_begin
							&& row_perm_inv[i.zx()].zx() < s_end
					});
					if contributes_to_u {
						let d_col_start =
							d_col_ind.partition_point(partition_fn(s_end));
						let d_LU = work_to_mat_mut(
							&mut left_contrib[d].0,
							d_row_idx.len(),
							d_col_ind.len(),
						);
						let mut d_LU = d_LU.get_mut(.., d_col_start..);
						let left_contrib = &mut left_contrib[d];
						let d_active = &mut left_contrib.1[d_col_start..];
						let d_active_count = &mut left_contrib.2;
						let d_active_mat = &mut left_contrib.3;
						for (d_j, j) in
							d_col_ind[d_col_start..].iter().enumerate()
						{
							if d_active[d_j] > I(0) {
								let mut taken_rows = 0usize;
								let j = j.zx();
								let s_j = col_global_to_local[j].zx();
								for (d_i, i) in d_row_idx.iter().enumerate() {
									let i = i.zx();
									let pi = row_perm_inv[i].zx();
									if pi >= s_begin && pi < s_end {
										let s_i = row_global_to_local[i].zx();
										s_U[(s_i, s_j)] -= &d_LU[(d_i, d_j)];
										d_LU[(d_i, d_j)] = zero::<T>();
										taken_rows += d_active_mat
											[(d_i, d_j + d_col_start)]
											as usize;
										d_active_mat
											[(d_i, d_j + d_col_start)] = 0;
									}
								}
								assert!(d_active[d_j] >= I(taken_rows));
								d_active[d_j] -= I(taken_rows);
								if d_active[d_j] == I(0) {
									assert!(*d_active_count > 0);
									*d_active_count -= 1;
								}
							}
						}
						if *d_active_count == 0 {
							left_contrib.0.clear();
							left_contrib.1 = alloc::vec::Vec::new();
							left_contrib.2 = 0;
							left_contrib.3 = MatU8::new();
						}
					}
				}
			});
			linalg::triangular_solve::solve_unit_lower_triangular_in_place(
				s_L.rb().subrows(0, s_size),
				s_U.rb_mut(),
				par,
			);
			if s_row_idx_count > s_size && s_col_index_count > 0 {
				resize_vec::<T>(
					&mut right_contrib[0].0,
					from_wide_checked(
						to_wide(I(s_row_idx_count - s_size))
							* to_wide(I(s_col_index_count)),
					)?
					.zx(),
					false,
					false,
					zero::<T>(),
				)?;
				right_contrib[0]
					.1
					.try_reserve_exact(s_col_index_count)
					.ok()
					.ok_or(FaerError::OutOfMemory)?;
				right_contrib[0]
					.1
					.resize(s_col_index_count, I(s_row_idx_count - s_size));
				right_contrib[0].2 = s_col_index_count;
				right_contrib[0].3 = MatU8::with_dims(
					s_row_idx_count - s_size,
					s_col_index_count,
				)?;
				let mut s_LU = work_to_mat_mut(
					&mut right_contrib[0].0,
					s_row_idx_count - s_size,
					s_col_index_count,
				);
				linalg::matmul::matmul(
					s_LU.rb_mut(),
					Accum::Replace,
					s_L.rb().get(s_size.., ..),
					s_U.rb(),
					one::<T>(),
					par,
				);
				noinline(Front, || {
					for d in &supernode_postorder
						[s_postordered - desc_count..s_postordered]
					{
						let d = d.zx();
						if left_contrib[d].0.is_empty() {
							continue;
						}
						let d_begin = supernode_ptr[d].zx();
						let d_end = supernode_ptr[d + 1].zx();
						let d_size = d_end - d_begin;
						let d_row_idx =
							&left_row_idxices[lu.l_col_ptr_for_row_idx[d].zx()
								..lu.l_col_ptr_for_row_idx[d + 1].zx()][d_size..];
						let d_col_ind =
							&lu.ut_row_idx[lu.ut_col_ptr_for_row_idx[d].zx()
								..lu.ut_col_ptr_for_row_idx[d + 1].zx()];
						let contributes_to_front = d_row_idx
							.iter()
							.any(|&i| row_perm_inv[i.zx()].zx() >= s_end);
						if contributes_to_front {
							let d_col_start =
								d_col_ind.partition_point(partition_fn(s_end));
							let d_LU = work_to_mat_mut(
								&mut left_contrib[d].0,
								d_row_idx.len(),
								d_col_ind.len(),
							);
							let mut d_LU = d_LU.get_mut(.., d_col_start..);
							let left_contrib = &mut left_contrib[d];
							let d_active = &mut left_contrib.1[d_col_start..];
							let d_active_count = &mut left_contrib.2;
							let d_active_mat = &mut left_contrib.3;
							let mut d_active_row_count = 0usize;
							let mut first_iter = true;
							for (d_j, j) in
								d_col_ind[d_col_start..].iter().enumerate()
							{
								if d_active[d_j] > I(0) {
									if first_iter {
										first_iter = false;
										for (d_i, i) in
											d_row_idx.iter().enumerate()
										{
											let i = i.zx();
											let pi = row_perm_inv[i].zx();
											if (pi < s_end)
												|| (row_global_to_local[i]
													== I(NONE))
											{
												continue;
											}
											d_active_rows[d_active_row_count] =
												I(d_i);
											d_active_row_count += 1;
										}
									}
									let j = j.zx();
									let mut taken_rows = 0usize;
									let s_j = col_global_to_local[j];
									if s_j == I(NONE) {
										continue;
									}
									let s_j = s_j.zx();
									let mut dst = s_LU.rb_mut().col_mut(s_j);
									let mut src = d_LU.rb_mut().col_mut(d_j);
									assert!(dst.row_stride() == 1);
									assert!(src.row_stride() == 1);
									for d_i in
										&d_active_rows[..d_active_row_count]
									{
										let d_i = d_i.zx();
										let i = d_row_idx[d_i].zx();
										let d_active_mat = &mut d_active_mat
											[(d_i, d_j + d_col_start)];
										if *d_active_mat == 0 {
											continue;
										}
										let s_i = row_global_to_local[i].zx()
											- s_size;
										dst[s_i] += &src[d_i];
										src[d_i] = zero::<T>();
										taken_rows += 1;
										*d_active_mat = 0;
									}
									d_active[d_j] -= I(taken_rows);
									if d_active[d_j] == I(0) {
										*d_active_count -= 1;
									}
								}
							}
							if *d_active_count == 0 {
								left_contrib.0.clear();
								left_contrib.1 = alloc::vec::Vec::new();
								left_contrib.2 = 0;
								left_contrib.3 = MatU8::new();
							}
						}
					}
				})
			}
			for i in s_row_idxices.iter() {
				row_global_to_local[i.zx()] = I(NONE);
			}
			for j in s_col_indices.iter() {
				col_global_to_local[j.zx()] = I(NONE);
			}
		}
		assert!(A_leftover == 0);
		for idx in
			&mut lu.l_row_idx[..lu.l_col_ptr_for_row_idx[n_supernodes].zx()]
		{
			*idx = row_perm_inv[idx.zx()];
		}
		lu.nrows = m;
		lu.ncols = n;
		lu.nsupernodes = n_supernodes;
		lu.supernode_ptr.clone_from(supernode_ptr);
		Ok(())
	}
}

/// simplicial factorization module
///
/// a supernodal factorization is one that processes the elements of the $LU$
/// factors of the input matrix by single elements, rather than by blocks. this
/// is more efficient if the lu factors are very sparse
pub mod simplicial {
	use super::*;
	use crate::assert;

	pub fn solve_in_place_scratch<I: Index, T: ComplexField>(
		dim: usize,
		rhs_ncols: usize,
		par: Par,
	) -> StackReq {
		let _ = par;
		crate::perm::permute_rows_in_place_scratch::<I, T>(dim, rhs_ncols)
	}

	pub fn solve_transpose_in_place_scratch<I: Index, T: ComplexField>(
		dim: usize,
		rhs_ncols: usize,
		par: Par,
	) -> StackReq {
		solve_in_place_scratch::<I, T>(dim, rhs_ncols, par)
	}

	/// $LU$ factor structure containing the symbolic and numerical
	/// representations
	#[derive(Debug, Clone)]
	pub struct SimplicialLu<I, T> {
		nrows: usize,
		ncols: usize,
		l_col_ptr: alloc::vec::Vec<I>,
		l_row_idx: alloc::vec::Vec<I>,
		l_val: alloc::vec::Vec<T>,
		u_col_ptr: alloc::vec::Vec<I>,
		u_row_idx: alloc::vec::Vec<I>,
		u_val: alloc::vec::Vec<T>,
	}
	impl<I: Index, T> Default for SimplicialLu<I, T> {
		fn default() -> Self {
			Self::new()
		}
	}
	impl<I: Index, T> SimplicialLu<I, T> {
		/// creates a new simplicial $LU$ of a $0 \times 0$ matrix
		#[inline]
		pub fn new() -> Self {
			Self {
				nrows: 0,
				ncols: 0,
				l_col_ptr: alloc::vec::Vec::new(),
				u_col_ptr: alloc::vec::Vec::new(),
				l_row_idx: alloc::vec::Vec::new(),
				u_row_idx: alloc::vec::Vec::new(),
				l_val: alloc::vec::Vec::new(),
				u_val: alloc::vec::Vec::new(),
			}
		}

		/// returns the number of rows of $A$
		#[inline]
		pub fn nrows(&self) -> usize {
			self.nrows
		}

		/// returns the number of columns of $A$
		#[inline]
		pub fn ncols(&self) -> usize {
			self.ncols
		}

		/// returns the $L$ factor of the $LU$ factorization. the row indices
		/// may be unsorted
		#[inline]
		pub fn l_factor_unsorted(&self) -> SparseColMatRef<'_, I, T> {
			SparseColMatRef::<'_, I, T>::new(
				unsafe {
					SymbolicSparseColMatRef::new_unchecked(
						self.nrows(),
						self.ncols(),
						&self.l_col_ptr,
						None,
						&self.l_row_idx,
					)
				},
				&self.l_val,
			)
		}

		/// returns the $U$ factor of the $LU$ factorization. the row indices
		/// may be unsorted
		#[inline]
		pub fn u_factor_unsorted(&self) -> SparseColMatRef<'_, I, T> {
			SparseColMatRef::<'_, I, T>::new(
				unsafe {
					SymbolicSparseColMatRef::new_unchecked(
						self.ncols(),
						self.ncols(),
						&self.u_col_ptr,
						None,
						&self.u_row_idx,
					)
				},
				&self.u_val,
			)
		}

		/// solves the equation $A x = \text{rhs}$ and stores the result in
		/// `rhs`, implicitly conjugating $A$ if needed
		///
		/// # panics
		/// - panics if `self.nrows() != self.ncols()`
		/// - panics if `rhs.nrows() != self.nrows()`
		#[track_caller]
		pub fn solve_in_place_with_conj(
			&self,
			row_perm: PermRef<'_, I>,
			col_perm: PermRef<'_, I>,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			assert!(self.nrows() == self.ncols());
			assert!(self.nrows() == rhs.nrows());
			let mut X = rhs;
			let l = self.l_factor_unsorted();
			let u = self.u_factor_unsorted();
			crate::perm::permute_rows_in_place(X.rb_mut(), row_perm, stack);
			linalg_sp::triangular_solve::solve_unit_lower_triangular_in_place(
				l,
				conj_lhs,
				X.rb_mut(),
				par,
			);
			linalg_sp::triangular_solve::solve_upper_triangular_in_place(
				u,
				conj_lhs,
				X.rb_mut(),
				par,
			);
			crate::perm::permute_rows_in_place(
				X.rb_mut(),
				col_perm.inverse(),
				stack,
			);
		}

		/// solves the equation $A^\top x = \text{rhs}$ and stores the result in
		/// `rhs`, implicitly conjugating $A$ if needed
		///
		/// # panics
		/// - panics if `self.nrows() != self.ncols()`
		/// - panics if `rhs.nrows() != self.nrows()`
		#[track_caller]
		pub fn solve_transpose_in_place_with_conj(
			&self,
			row_perm: PermRef<'_, I>,
			col_perm: PermRef<'_, I>,
			conj_lhs: Conj,
			rhs: MatMut<'_, T>,
			par: Par,
			stack: &mut MemStack,
		) where
			T: ComplexField,
		{
			assert!(all(
				self.nrows() == self.ncols(),
				self.nrows() == rhs.nrows()
			));
			let mut X = rhs;
			let l = self.l_factor_unsorted();
			let u = self.u_factor_unsorted();
			crate::perm::permute_rows_in_place(X.rb_mut(), col_perm, stack);
			linalg_sp::triangular_solve::solve_upper_triangular_transpose_in_place(u, conj_lhs, X.rb_mut(), par);
			linalg_sp::triangular_solve::solve_unit_lower_triangular_transpose_in_place(l, conj_lhs, X.rb_mut(), par);
			crate::perm::permute_rows_in_place(
				X.rb_mut(),
				row_perm.inverse(),
				stack,
			);
		}
	}
	fn depth_first_search<I: Index>(
		marked: &mut [I],
		mark: I,
		xi: &mut [I],
		l: SymbolicSparseColMatRef<'_, I>,
		row_perm_inv: &[I],
		b: usize,
		stack: &mut [I],
	) -> usize {
		let I = I::truncate;
		let mut tail_start = xi.len();
		let mut head_len = 1usize;
		xi[0] = I(b);
		let li = l.row_idx();
		'dfs_loop: while head_len > 0 {
			let b = xi[head_len - 1].zx().zx();
			let pb = row_perm_inv[b].zx();
			let range = if pb < l.ncols() {
				l.col_range(pb)
			} else {
				0..0
			};
			if marked[b] < mark {
				marked[b] = mark;
				stack[head_len - 1] = I(range.start);
			}
			let start = stack[head_len - 1].zx();
			let end = range.end;
			for ptr in start..end {
				let i = li[ptr].zx();
				if marked[i] == mark {
					continue;
				}
				stack[head_len - 1] = I(ptr);
				xi[head_len] = I(i);
				head_len += 1;
				continue 'dfs_loop;
			}
			head_len -= 1;
			tail_start -= 1;
			xi[tail_start] = I(b);
		}
		tail_start
	}
	fn reach<I: Index>(
		marked: &mut [I],
		mark: I,
		xi: &mut [I],
		l: SymbolicSparseColMatRef<'_, I>,
		row_perm_inv: &[I],
		bi: &[I],
		stack: &mut [I],
	) -> usize {
		let n = l.nrows();
		let mut tail_start = n;
		for b in bi {
			let b = b.zx();
			if marked[b] < mark {
				tail_start = depth_first_search(
					marked,
					mark,
					&mut xi[..tail_start],
					l,
					row_perm_inv,
					b,
					stack,
				);
			}
		}
		tail_start
	}
	fn l_incomplete_solve_sparse<I: Index, T: ComplexField>(
		marked: &mut [I],
		mark: I,
		xi: &mut [I],
		x: &mut [T],
		l: SparseColMatRef<'_, I, T>,
		row_perm_inv: &[I],
		bi: &[I],
		bx: &[T],
		stack: &mut [I],
	) -> usize {
		let tail_start =
			reach(marked, mark, xi, l.symbolic(), row_perm_inv, bi, stack);
		let xi = &xi[tail_start..];
		for (i, b) in iter::zip(bi, bx) {
			let i = i.zx();
			x[i] += b;
		}
		for i in xi {
			let i = i.zx();
			let pi = row_perm_inv[i].zx();
			if pi >= l.ncols() {
				continue;
			}
			let li = l.row_idx_of_col_raw(pi);
			let lx = l.val_of_col(pi);
			let len = li.len();
			let ref xi = x[i].copy();
			for (li, lx) in iter::zip(&li[1..], &lx[1..len]) {
				let li = li.zx();
				x[li] -= lx * xi;
			}
		}
		tail_start
	}
	/// computes the layout of the workspace required to perform a numeric $LU$
	/// factorization
	pub fn factorize_simplicial_numeric_lu_scratch<
		I: Index,
		T: ComplexField,
	>(
		nrows: usize,
		ncols: usize,
	) -> StackReq {
		let idx = StackReq::new::<I>(nrows);
		let val = temp_mat_scratch::<T>(nrows, 1);
		let _ = ncols;
		StackReq::all_of(&[val, idx, idx, idx])
	}
	/// computes the numeric values of the $LU$ factors of the matrix $A$ as
	/// well as the row pivoting permutation, and stores them in `lu` and
	/// `row_perm`/`row_perm_inv`
	pub fn factorize_simplicial_numeric_lu<I: Index, T: ComplexField>(
		row_perm: &mut [I],
		row_perm_inv: &mut [I],
		lu: &mut SimplicialLu<I, T>,
		A: SparseColMatRef<'_, I, T>,
		col_perm: PermRef<'_, I>,
		stack: &mut MemStack,
	) -> Result<(), LuError> {
		let I = I::truncate;
		assert!(all(
			A.nrows() == row_perm.len(),
			A.nrows() == row_perm_inv.len(),
			A.ncols() == col_perm.len(),
			A.nrows() == A.ncols()
		));
		lu.nrows = 0;
		lu.ncols = 0;
		let m = A.nrows();
		let n = A.ncols();
		resize_vec(&mut lu.l_col_ptr, n + 1, true, false, I(0))?;
		resize_vec(&mut lu.u_col_ptr, n + 1, true, false, I(0))?;
		let (mut x, stack) = temp_mat_zeroed::<T, _, _>(m, 1, stack);
		let x = x
			.as_mat_mut()
			.col_mut(0)
			.try_as_col_major_mut()
			.unwrap()
			.as_slice_mut();
		let (marked, stack) = unsafe { stack.make_raw::<I>(m) };
		let (xj, stack) = unsafe { stack.make_raw::<I>(m) };
		let (stack, _) = unsafe { stack.make_raw::<I>(m) };
		marked.fill(I(0));
		row_perm_inv.fill(I(n));
		let mut l_pos = 0usize;
		let mut u_pos = 0usize;
		lu.l_col_ptr[0] = I(0);
		lu.u_col_ptr[0] = I(0);
		for j in 0..n {
			let l = SparseColMatRef::<'_, I, T>::new(
				unsafe {
					SymbolicSparseColMatRef::new_unchecked(
						m,
						j,
						&lu.l_col_ptr[..j + 1],
						None,
						&lu.l_row_idx,
					)
				},
				&lu.l_val,
			);
			let pj = col_perm.arrays().0[j].zx();
			let tail_start = l_incomplete_solve_sparse(
				marked,
				I(j + 1),
				xj,
				x,
				l,
				row_perm_inv,
				A.row_idx_of_col_raw(pj),
				A.val_of_col(pj),
				stack,
			);
			let xj = &xj[tail_start..];
			resize_vec::<T>(
				&mut lu.l_val,
				l_pos + xj.len() + 1,
				false,
				false,
				zero::<T>(),
			)?;
			resize_vec(
				&mut lu.l_row_idx,
				l_pos + xj.len() + 1,
				false,
				false,
				I(0),
			)?;
			resize_vec::<T>(
				&mut lu.u_val,
				u_pos + xj.len() + 1,
				false,
				false,
				zero::<T>(),
			)?;
			resize_vec(
				&mut lu.u_row_idx,
				u_pos + xj.len() + 1,
				false,
				false,
				I(0),
			)?;
			let l_val = &mut *lu.l_val;
			let u_val = &mut *lu.u_val;
			let mut pivot_idx = n;
			let mut pivot_val = -one::<T::Real>();
			for i in xj {
				let i = i.zx();
				let xi = x[i].copy();
				if row_perm_inv[i] == I(n) {
					let val = xi.abs();
					if matches!(
						val.partial_cmp(&pivot_val),
						None | Some(core::cmp::Ordering::Greater)
					) {
						pivot_idx = i;
						pivot_val = val;
					}
				} else {
					lu.u_row_idx[u_pos] = row_perm_inv[i];
					u_val[u_pos] = xi;
					u_pos += 1;
				}
			}
			if pivot_idx == n {
				return Err(LuError::SymbolicSingular { index: j });
			}
			let x_piv = x[pivot_idx].copy();
			let ref x_piv_inv = x_piv.recip();
			row_perm_inv[pivot_idx] = I(j);
			lu.u_row_idx[u_pos] = I(j);
			u_val[u_pos] = x_piv;
			u_pos += 1;
			lu.u_col_ptr[j + 1] = I(u_pos);
			lu.l_row_idx[l_pos] = I(pivot_idx);
			l_val[l_pos] = one::<T>();
			l_pos += 1;
			for i in xj {
				let i = i.zx();
				let xi = x[i].copy();
				if row_perm_inv[i] == I(n) {
					lu.l_row_idx[l_pos] = I(i);
					l_val[l_pos] = xi * x_piv_inv;
					l_pos += 1;
				}
				x[i] = zero::<T>();
			}
			lu.l_col_ptr[j + 1] = I(l_pos);
		}
		for i in &mut lu.l_row_idx[..l_pos] {
			*i = row_perm_inv[(*i).zx()];
		}
		for (idx, p) in row_perm_inv.iter().enumerate() {
			row_perm[p.zx()] = I(idx);
		}
		lu.nrows = m;
		lu.ncols = n;
		Ok(())
	}
}
/// tuning parameters for the $LU$ symbolic factorization
#[derive(Copy, Clone, Debug, Default)]
pub struct LuSymbolicParams<'a> {
	/// parameters for the fill reducing column permutation
	pub colamd_params: colamd::Control,
	/// threshold for selecting the supernodal factorization
	pub supernodal_flop_ratio_threshold: SupernodalThreshold,
	/// supernodal factorization parameters
	pub supernodal_params: SymbolicSupernodalParams<'a>,
}
/// the inner factorization used for the symbolic $LU$, either simplicial or
/// symbolic
#[derive(Debug, Clone)]
pub enum SymbolicLuRaw<I> {
	/// simplicial structure
	Simplicial {
		/// number of rows of $A$
		nrows: usize,
		/// number of columns of $A$
		ncols: usize,
	},
	/// supernodal structure
	Supernodal(supernodal::SymbolicSupernodalLu<I>),
}
/// the symbolic structure of a sparse $LU$ decomposition
#[derive(Debug, Clone)]
pub struct SymbolicLu<I> {
	raw: SymbolicLuRaw<I>,
	col_perm_fwd: alloc::vec::Vec<I>,
	col_perm_inv: alloc::vec::Vec<I>,
	A_nnz: usize,
}
#[derive(Debug, Clone)]
enum NumericLuRaw<I, T> {
	None,
	Supernodal(supernodal::SupernodalLu<I, T>),
	Simplicial(simplicial::SimplicialLu<I, T>),
}
/// structure that contains the numerical values and row pivoting permutation of
/// the lu decomposition
#[derive(Debug, Clone)]
pub struct NumericLu<I, T> {
	raw: NumericLuRaw<I, T>,
	row_perm_fwd: alloc::vec::Vec<I>,
	row_perm_inv: alloc::vec::Vec<I>,
}
impl<I: Index, T> Default for NumericLu<I, T> {
	fn default() -> Self {
		Self::new()
	}
}
impl<I: Index, T> NumericLu<I, T> {
	/// creates a new $LU$ of a $0\times 0$ matrix
	#[inline]
	pub fn new() -> Self {
		Self {
			raw: NumericLuRaw::None,
			row_perm_fwd: alloc::vec::Vec::new(),
			row_perm_inv: alloc::vec::Vec::new(),
		}
	}
}
/// sparse $LU$ factorization wrapper
#[derive(Debug)]
pub struct LuRef<'a, I: Index, T> {
	symbolic: &'a SymbolicLu<I>,
	numeric: &'a NumericLu<I, T>,
}
impl<I: Index, T> Copy for LuRef<'_, I, T> {}
impl<I: Index, T> Clone for LuRef<'_, I, T> {
	fn clone(&self) -> Self {
		*self
	}
}
impl<'a, I: Index, T> LuRef<'a, I, T> {
	/// creates $LU$ factors from their components
	///
	/// # safety
	/// the numeric part must be the output of
	/// [`SymbolicLu::factorize_numeric_lu`], called with a matrix having the
	/// same symbolic structure as the one used to create `symbolic`
	#[inline]
	pub fn new_unchecked(
		symbolic: &'a SymbolicLu<I>,
		numeric: &'a NumericLu<I, T>,
	) -> Self {
		match (&symbolic.raw, &numeric.raw) {
			(SymbolicLuRaw::Simplicial { .. }, NumericLuRaw::Simplicial(_)) => {
			},
			(SymbolicLuRaw::Supernodal { .. }, NumericLuRaw::Supernodal(_)) => {
			},
			_ => panic!("incompatible symbolic and numeric variants"),
		}
		Self { symbolic, numeric }
	}

	/// returns the symbolic structure of the $LU$ factorization
	#[inline]
	pub fn symbolic(self) -> &'a SymbolicLu<I> {
		self.symbolic
	}

	/// returns the row pivoting permutation
	#[inline]
	pub fn row_perm(self) -> PermRef<'a, I> {
		unsafe {
			PermRef::new_unchecked(
				&self.numeric.row_perm_fwd,
				&self.numeric.row_perm_inv,
				self.symbolic.nrows(),
			)
		}
	}

	/// returns the fill reducing column permutation
	#[inline]
	pub fn col_perm(self) -> PermRef<'a, I> {
		self.symbolic.col_perm()
	}

	/// solves the equation $A x = \text{rhs}$ and stores the result in `rhs`,
	/// implicitly conjugating $A$ if needed
	///
	/// # panics
	/// - panics if `self.nrows() != self.ncols()`
	/// - panics if `rhs.nrows() != self.nrows()`
	#[track_caller]
	pub fn solve_in_place_with_conj(
		self,
		conj: Conj,
		rhs: MatMut<'_, T>,
		par: Par,
		stack: &mut MemStack,
	) where
		T: ComplexField,
	{
		match (&self.symbolic.raw, &self.numeric.raw) {
			(
				SymbolicLuRaw::Simplicial { .. },
				NumericLuRaw::Simplicial(numeric),
			) => numeric.solve_in_place_with_conj(
				self.row_perm(),
				self.col_perm(),
				conj,
				rhs,
				par,
				stack,
			),
			(
				SymbolicLuRaw::Supernodal(_),
				NumericLuRaw::Supernodal(numeric),
			) => numeric.solve_in_place_with_conj(
				self.row_perm(),
				self.col_perm(),
				conj,
				rhs,
				par,
				stack,
			),
			_ => unreachable!(),
		}
	}

	/// solves the equation $A^\top x = \text{rhs}$ and stores the result in
	/// `rhs`, implicitly conjugating $A$ if needed
	///
	/// # panics
	/// - panics if `self.nrows() != self.ncols()`
	/// - panics if `rhs.nrows() != self.nrows()`
	#[track_caller]
	pub fn solve_transpose_in_place_with_conj(
		self,
		conj: Conj,
		rhs: MatMut<'_, T>,
		par: Par,
		stack: &mut MemStack,
	) where
		T: ComplexField,
	{
		match (&self.symbolic.raw, &self.numeric.raw) {
			(
				SymbolicLuRaw::Simplicial { .. },
				NumericLuRaw::Simplicial(numeric),
			) => numeric.solve_transpose_in_place_with_conj(
				self.row_perm(),
				self.col_perm(),
				conj,
				rhs,
				par,
				stack,
			),
			(
				SymbolicLuRaw::Supernodal(_),
				NumericLuRaw::Supernodal(numeric),
			) => numeric.solve_transpose_in_place_with_conj(
				self.row_perm(),
				self.col_perm(),
				conj,
				rhs,
				par,
				stack,
			),
			_ => unreachable!(),
		}
	}
}
impl<I: Index> SymbolicLu<I> {
	/// returns the number of rows of $A$
	#[inline]
	pub fn nrows(&self) -> usize {
		match &self.raw {
			SymbolicLuRaw::Simplicial { nrows, .. } => *nrows,
			SymbolicLuRaw::Supernodal(this) => this.nrows,
		}
	}

	/// returns the number of columns of $A$
	#[inline]
	pub fn ncols(&self) -> usize {
		match &self.raw {
			SymbolicLuRaw::Simplicial { ncols, .. } => *ncols,
			SymbolicLuRaw::Supernodal(this) => this.ncols,
		}
	}

	/// returns the fill-reducing column permutation that was computed during
	/// symbolic analysis
	#[inline]
	pub fn col_perm(&self) -> PermRef<'_, I> {
		unsafe {
			PermRef::new_unchecked(
				&self.col_perm_fwd,
				&self.col_perm_inv,
				self.ncols(),
			)
		}
	}

	/// computes the layout of the workspace required to compute the numerical
	/// $LU$ factorization
	pub fn factorize_numeric_lu_scratch<T>(
		&self,
		par: Par,
		params: Spec<PartialPivLuParams, T>,
	) -> StackReq
	where
		T: ComplexField,
	{
		match &self.raw {
			SymbolicLuRaw::Simplicial { nrows, ncols } => {
				simplicial::factorize_simplicial_numeric_lu_scratch::<I, T>(
					*nrows, *ncols,
				)
			},
			SymbolicLuRaw::Supernodal(symbolic) => {
				let _ = par;
				let m = symbolic.nrows;
				let A_nnz = self.A_nnz;
				let AT_scratch = StackReq::all_of(&[
					temp_mat_scratch::<T>(A_nnz, 1),
					StackReq::new::<I>(m + 1),
					StackReq::new::<I>(A_nnz),
				]);
				StackReq::and(
					AT_scratch,
					supernodal::factorize_supernodal_numeric_lu_scratch::<I, T>(
						symbolic, params,
					),
				)
			},
		}
	}

	/// computes the layout of the workspace required to solve the equation $A x
	/// = b$
	pub fn solve_in_place_scratch<T>(
		&self,
		rhs_ncols: usize,
		par: Par,
	) -> StackReq
	where
		T: ComplexField,
	{
		match &self.raw {
			SymbolicLuRaw::Simplicial { nrows, .. } => {
				simplicial::solve_in_place_scratch::<I, T>(
					*nrows, rhs_ncols, par,
				)
			},
			SymbolicLuRaw::Supernodal(this) => {
				supernodal::solve_in_place_scratch::<I, T>(
					this.nrows, rhs_ncols, par,
				)
			},
		}
	}

	/// computes the layout of the workspace required to solve the equation
	/// $A^\top x = b$
	pub fn solve_transpose_in_place_scratch<T>(
		&self,
		rhs_ncols: usize,
		par: Par,
	) -> StackReq
	where
		T: ComplexField,
	{
		self.solve_in_place_scratch::<T>(rhs_ncols, par)
	}

	/// computes a numerical $LU$ factorization of $A$
	#[track_caller]
	pub fn factorize_numeric_lu<'out, T: ComplexField>(
		&'out self,
		numeric: &'out mut NumericLu<I, T>,
		A: SparseColMatRef<'_, I, T>,
		par: Par,
		stack: &mut MemStack,
		params: Spec<PartialPivLuParams, T>,
	) -> Result<LuRef<'out, I, T>, LuError> {
		if matches!(self.raw, SymbolicLuRaw::Simplicial { .. })
			&& !matches!(numeric.raw, NumericLuRaw::Simplicial(_))
		{
			numeric.raw =
				NumericLuRaw::Simplicial(simplicial::SimplicialLu::new());
		}
		if matches!(self.raw, SymbolicLuRaw::Supernodal(_))
			&& !matches!(numeric.raw, NumericLuRaw::Supernodal(_))
		{
			numeric.raw =
				NumericLuRaw::Supernodal(supernodal::SupernodalLu::new());
		}
		let nrows = self.nrows();
		numeric
			.row_perm_fwd
			.try_reserve_exact(nrows.saturating_sub(numeric.row_perm_fwd.len()))
			.ok()
			.ok_or(FaerError::OutOfMemory)?;
		numeric
			.row_perm_inv
			.try_reserve_exact(nrows.saturating_sub(numeric.row_perm_inv.len()))
			.ok()
			.ok_or(FaerError::OutOfMemory)?;
		numeric.row_perm_fwd.resize(nrows, I::truncate(0));
		numeric.row_perm_inv.resize(nrows, I::truncate(0));
		match (&self.raw, &mut numeric.raw) {
			(
				SymbolicLuRaw::Simplicial { nrows, ncols },
				NumericLuRaw::Simplicial(lu),
			) => {
				assert!(all(A.nrows() == *nrows, A.ncols() == *ncols));
				simplicial::factorize_simplicial_numeric_lu(
					&mut numeric.row_perm_fwd,
					&mut numeric.row_perm_inv,
					lu,
					A,
					self.col_perm(),
					stack,
				)?;
			},
			(
				SymbolicLuRaw::Supernodal(symbolic),
				NumericLuRaw::Supernodal(lu),
			) => {
				let m = symbolic.nrows;
				let (new_col_ptr, stack) =
					unsafe { stack.make_raw::<I>(m + 1) };
				let (new_row_idx, stack) =
					unsafe { stack.make_raw::<I>(self.A_nnz) };
				let (mut new_values, stack) =
					unsafe { temp_mat_uninit::<T, _, _>(self.A_nnz, 1, stack) };
				let new_values = new_values
					.as_mat_mut()
					.col_mut(0)
					.try_as_col_major_mut()
					.unwrap()
					.as_slice_mut();
				let AT = utils::transpose(
					new_values,
					new_col_ptr,
					new_row_idx,
					A,
					stack,
				)
				.into_const();
				supernodal::factorize_supernodal_numeric_lu(
					&mut numeric.row_perm_fwd,
					&mut numeric.row_perm_inv,
					lu,
					A,
					AT,
					self.col_perm(),
					symbolic,
					par,
					stack,
					params,
				)?;
			},
			_ => unreachable!(),
		}
		Ok(LuRef::new_unchecked(self, numeric))
	}
}
/// computes the symbolic $LU$ factorization of the matrix $A$, or returns an
/// error if the operation could not be completed
#[track_caller]
pub fn factorize_symbolic_lu<I: Index>(
	A: SymbolicSparseColMatRef<'_, I>,
	params: LuSymbolicParams<'_>,
) -> Result<SymbolicLu<I>, FaerError> {
	assert!(A.nrows() == A.ncols());
	let m = A.nrows();
	let n = A.ncols();
	let A_nnz = A.compute_nnz();
	with_dim!(M, m);
	with_dim!(N, n);
	let A = A.as_shape(M, N);
	let req = {
		let n_scratch = StackReq::new::<I>(n);
		let m_scratch = StackReq::new::<I>(m);
		let AT_scratch =
			StackReq::and(StackReq::new::<I>(m + 1), StackReq::new::<I>(A_nnz));
		StackReq::or(
			linalg_sp::colamd::order_scratch::<I>(m, n, A_nnz),
			StackReq::all_of(&[
				n_scratch,
				n_scratch,
				n_scratch,
				n_scratch,
				AT_scratch,
				StackReq::any_of(&[
					StackReq::and(n_scratch, m_scratch),
					StackReq::all_of(&[n_scratch; 3]),
					StackReq::all_of(&[
						n_scratch, n_scratch, n_scratch, n_scratch, n_scratch,
						m_scratch,
					]),
					supernodal::factorize_supernodal_symbolic_lu_scratch::<I>(
						m, n,
					),
				]),
			]),
		)
	};
	let mut mem = dyn_stack::MemBuffer::try_new(req)
		.ok()
		.ok_or(FaerError::OutOfMemory)?;
	let stack = MemStack::new(&mut mem);
	let mut col_perm_fwd = try_zeroed::<I>(n)?;
	let mut col_perm_inv = try_zeroed::<I>(n)?;
	let mut min_row = try_zeroed::<I>(m)?;
	linalg_sp::colamd::order(
		&mut col_perm_fwd,
		&mut col_perm_inv,
		A.as_dyn(),
		params.colamd_params,
		stack,
	)?;
	let col_perm =
		PermRef::new_checked(&col_perm_fwd, &col_perm_inv, n).as_shape(N);
	let (new_col_ptr, stack) = unsafe { stack.make_raw::<I>(m + 1) };
	let (new_row_idx, stack) = unsafe { stack.make_raw::<I>(A_nnz) };
	let AT = utils::adjoint(
		Symbolic::materialize(new_row_idx.len()),
		new_col_ptr,
		new_row_idx,
		SparseColMatRef::new(A, Symbolic::materialize(A.row_idx().len())),
		stack,
	)
	.symbolic();
	let (etree, stack) = unsafe { stack.make_raw::<I::Signed>(n) };
	let (post, stack) = unsafe { stack.make_raw::<I>(n) };
	let (col_counts, stack) = unsafe { stack.make_raw::<I>(n) };
	let (h_col_counts, stack) = unsafe { stack.make_raw::<I>(n) };
	linalg_sp::qr::ghost_col_etree(
		A,
		Some(col_perm),
		Array::from_mut(etree, N),
		stack,
	);
	let etree_ =
		Array::from_ref(MaybeIdx::<'_, I>::from_slice_ref_checked(etree, N), N);
	linalg_sp::cholesky::ghost_postorder(
		Array::from_mut(post, N),
		etree_,
		stack,
	);
	linalg_sp::qr::ghost_column_counts_aat(
		Array::from_mut(col_counts, N),
		Array::from_mut(bytemuck::cast_slice_mut(&mut min_row), M),
		AT,
		Some(col_perm),
		etree_,
		Array::from_ref(Idx::from_slice_ref_checked(post, N), N),
		stack,
	);
	let min_col = min_row;
	let mut threshold = params.supernodal_flop_ratio_threshold;
	if threshold != SupernodalThreshold::FORCE_SIMPLICIAL
		&& threshold != SupernodalThreshold::FORCE_SUPERNODAL
	{
		h_col_counts.fill(I::truncate(0));
		for i in 0..m {
			let min_col = min_col[i];
			if min_col.to_signed() < I::Signed::truncate(0) {
				continue;
			}
			h_col_counts[min_col.zx()] += I::truncate(1);
		}
		for j in 0..n {
			let parent = etree[j];
			if parent < I::Signed::truncate(0) {
				continue;
			}
			h_col_counts[parent.zx()] += h_col_counts[j] - I::truncate(1);
		}
		let mut nnz = 0.0f64;
		let mut flops = 0.0f64;
		for j in 0..n {
			let hj = h_col_counts[j].zx() as f64;
			let rj = col_counts[j].zx() as f64;
			flops += hj + hj * rj;
			nnz += hj + rj;
		}
		if flops / nnz > threshold.0 * linalg_sp::LU_SUPERNODAL_RATIO_FACTOR {
			threshold = SupernodalThreshold::FORCE_SUPERNODAL;
		} else {
			threshold = SupernodalThreshold::FORCE_SIMPLICIAL;
		}
	}
	if threshold == SupernodalThreshold::FORCE_SUPERNODAL {
		let symbolic = supernodal::factorize_supernodal_symbolic_lu::<I>(
			A.as_dyn(),
			Some(col_perm.as_shape(n)),
			&min_col,
			EliminationTreeRef::<'_, I> { inner: etree },
			col_counts,
			stack,
			params.supernodal_params,
		)?;
		Ok(SymbolicLu {
			raw: SymbolicLuRaw::Supernodal(symbolic),
			col_perm_fwd,
			col_perm_inv,
			A_nnz,
		})
	} else {
		Ok(SymbolicLu {
			raw: SymbolicLuRaw::Simplicial { nrows: m, ncols: n },
			col_perm_fwd,
			col_perm_inv,
			A_nnz,
		})
	}
}
#[cfg(test)]
mod tests {
	use super::*;
	use crate::assert;
	use crate::stats::prelude::*;
	use dyn_stack::MemBuffer;
	use linalg_sp::cholesky::tests::load_mtx;
	use matrix_market_rs::MtxData;
	use std::path::PathBuf;
	#[test]
	fn test_numeric_lu_multifrontal() {
		type T = c64;
		let (m, n, col_ptr, row_idx, val) = load_mtx::<usize>(
			MtxData::from_file(
				PathBuf::from(env!("CARGO_MANIFEST_DIR"))
					.join("test_data/sparse_lu/YAO.mtx"),
			)
			.unwrap(),
		);
		let mut rng = StdRng::seed_from_u64(0);
		let mut gen = || T::new(rng.random::<f64>(), rng.random::<f64>());
		let val = val.iter().map(|_| gen()).collect::<alloc::vec::Vec<_>>();
		let A = SparseColMatRef::<'_, usize, T>::new(
			SymbolicSparseColMatRef::new_checked(
				m, n, &col_ptr, None, &row_idx,
			),
			&val,
		);
		let mut row_perm = vec![0usize; n];
		let mut row_perm_inv = vec![0usize; n];
		let mut col_perm = vec![0usize; n];
		let mut col_perm_inv = vec![0usize; n];
		for i in 0..n {
			col_perm[i] = i;
			col_perm_inv[i] = i;
		}
		let col_perm =
			PermRef::<'_, usize>::new_checked(&col_perm, &col_perm_inv, n);
		let mut etree = vec![0usize; n];
		let mut min_col = vec![0usize; m];
		let mut col_counts = vec![0usize; n];
		let nnz = A.compute_nnz();
		let mut new_col_ptr = vec![0usize; m + 1];
		let mut new_row_idx = vec![0usize; nnz];
		let mut new_values = vec![zero::<T>(); nnz];
		let AT = utils::transpose(
			&mut *new_values,
			&mut new_col_ptr,
			&mut new_row_idx,
			A,
			MemStack::new(&mut MemBuffer::new(StackReq::new::<usize>(m))),
		)
		.into_const();
		let etree = {
			let mut post = vec![0usize; n];
			let etree = linalg_sp::qr::col_etree(
				A.symbolic(),
				Some(col_perm),
				&mut etree,
				MemStack::new(&mut MemBuffer::new(StackReq::new::<usize>(
					m + n,
				))),
			);
			linalg_sp::qr::postorder(
				&mut post,
				etree,
				MemStack::new(&mut MemBuffer::new(StackReq::new::<usize>(
					3 * n,
				))),
			);
			linalg_sp::qr::column_counts_ata(
				&mut col_counts,
				&mut min_col,
				AT.symbolic(),
				Some(col_perm),
				etree,
				&post,
				MemStack::new(&mut MemBuffer::new(StackReq::new::<usize>(
					5 * n + m,
				))),
			);
			etree
		};
		let symbolic = linalg_sp::lu::supernodal::factorize_supernodal_symbolic_lu::<usize>(
			A.symbolic(),
			Some(col_perm),
			&min_col,
			etree,
			&col_counts,
			MemStack::new(&mut MemBuffer::new(super::supernodal::factorize_supernodal_symbolic_lu_scratch::<usize>(
				m, n,
			))),
			linalg_sp::SymbolicSupernodalParams {
				relax: Some(&[(4, 1.0), (16, 0.8), (48, 0.1), (usize::MAX, 0.05)]),
			},
		)
		.unwrap();
		let mut lu = supernodal::SupernodalLu::<usize, T>::new();
		supernodal::factorize_supernodal_numeric_lu(
			&mut row_perm,
			&mut row_perm_inv,
			&mut lu,
			A,
			AT,
			col_perm,
			&symbolic,
			Par::Seq,
			MemStack::new(&mut MemBuffer::new(
				supernodal::factorize_supernodal_numeric_lu_scratch::<usize, T>(
					&symbolic,
					Default::default(),
				),
			)),
			Default::default(),
		)
		.unwrap();
		let k = 2;
		let rhs = Mat::from_fn(n, k, |_, _| gen());
		let A_dense = A.to_dense();
		let row_perm =
			PermRef::<'_, _>::new_checked(&row_perm, &row_perm_inv, m);
		{
			let mut x = rhs.clone();
			lu.solve_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::No,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					supernodal::solve_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((&A_dense * &x - &rhs).norm_max() < 1e-10);
		}
		{
			let mut x = rhs.clone();
			lu.solve_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::Yes,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					supernodal::solve_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((A_dense.conjugate() * &x - &rhs).norm_max() < 1e-10);
		}
		{
			let mut x = rhs.clone();
			lu.solve_transpose_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::No,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					supernodal::solve_transpose_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((A_dense.transpose() * &x - &rhs).norm_max() < 1e-10);
		}
		{
			let mut x = rhs.clone();
			lu.solve_transpose_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::Yes,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					supernodal::solve_transpose_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((A_dense.adjoint() * &x - &rhs).norm_max() < 1e-10);
		}
	}
	#[test]
	fn test_numeric_lu_simplicial() {
		type T = c64;
		let (m, n, col_ptr, row_idx, val) = load_mtx::<usize>(
			MtxData::from_file(
				PathBuf::from(env!("CARGO_MANIFEST_DIR"))
					.join("test_data/sparse_lu/YAO.mtx"),
			)
			.unwrap(),
		);
		let mut rng = StdRng::seed_from_u64(0);
		let mut gen = || T::new(rng.random::<f64>(), rng.random::<f64>());
		let val = val.iter().map(|_| gen()).collect::<alloc::vec::Vec<_>>();
		let A = SparseColMatRef::<'_, usize, T>::new(
			SymbolicSparseColMatRef::new_checked(
				m, n, &col_ptr, None, &row_idx,
			),
			&val,
		);
		let mut row_perm = vec![0usize; n];
		let mut row_perm_inv = vec![0usize; n];
		let mut col_perm = vec![0usize; n];
		let mut col_perm_inv = vec![0usize; n];
		for i in 0..n {
			col_perm[i] = i;
			col_perm_inv[i] = i;
		}
		let col_perm =
			PermRef::<'_, usize>::new_checked(&col_perm, &col_perm_inv, n);
		let mut lu = simplicial::SimplicialLu::<usize, T>::new();
		simplicial::factorize_simplicial_numeric_lu(
			&mut row_perm,
			&mut row_perm_inv,
			&mut lu,
			A,
			col_perm,
			MemStack::new(&mut MemBuffer::new(
				simplicial::factorize_simplicial_numeric_lu_scratch::<usize, T>(
					m, n,
				),
			)),
		)
		.unwrap();
		let k = 1;
		let rhs = Mat::from_fn(n, k, |_, _| gen());
		let A_dense = A.to_dense();
		let row_perm =
			PermRef::<'_, _>::new_checked(&row_perm, &row_perm_inv, m);
		{
			let mut x = rhs.clone();
			lu.solve_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::No,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					simplicial::solve_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((&A_dense * &x - &rhs).norm_max() < 1e-10);
		}
		{
			let mut x = rhs.clone();
			lu.solve_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::Yes,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					simplicial::solve_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((A_dense.conjugate() * &x - &rhs).norm_max() < 1e-10);
		}
		{
			let mut x = rhs.clone();
			lu.solve_transpose_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::No,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					simplicial::solve_transpose_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((A_dense.transpose() * &x - &rhs).norm_max() < 1e-10);
		}
		{
			let mut x = rhs.clone();
			lu.solve_transpose_in_place_with_conj(
				row_perm,
				col_perm,
				Conj::Yes,
				x.as_mut(),
				Par::Seq,
				MemStack::new(&mut MemBuffer::new(
					simplicial::solve_transpose_in_place_scratch::<usize, T>(
						n,
						k,
						Par::Seq,
					),
				)),
			);
			assert!((A_dense.adjoint() * &x - &rhs).norm_max() < 1e-10);
		}
	}
	#[test]
	fn test_solver_lu_simplicial() {
		type T = c64;
		let (m, n, col_ptr, row_idx, val) = load_mtx::<usize>(
			MtxData::from_file(
				PathBuf::from(env!("CARGO_MANIFEST_DIR"))
					.join("test_data/sparse_lu/YAO.mtx"),
			)
			.unwrap(),
		);
		let mut rng = StdRng::seed_from_u64(0);
		let mut gen = || T::new(rng.random::<f64>(), rng.random::<f64>());
		let val = val.iter().map(|_| gen()).collect::<alloc::vec::Vec<_>>();
		let A = SparseColMatRef::<'_, usize, T>::new(
			SymbolicSparseColMatRef::new_checked(
				m, n, &col_ptr, None, &row_idx,
			),
			&val,
		);
		let rhs = Mat::<T>::from_fn(m, 6, |_, _| gen());
		for supernodal_flop_ratio_threshold in [
			SupernodalThreshold::AUTO,
			SupernodalThreshold::FORCE_SUPERNODAL,
			SupernodalThreshold::FORCE_SIMPLICIAL,
		] {
			let symbolic = factorize_symbolic_lu(
				A.symbolic(),
				LuSymbolicParams {
					supernodal_flop_ratio_threshold,
					..Default::default()
				},
			)
			.unwrap();
			let mut numeric = NumericLu::<usize, T>::new();
			let lu = symbolic
				.factorize_numeric_lu(
					&mut numeric,
					A,
					Par::Seq,
					MemStack::new(&mut MemBuffer::new(
						symbolic.factorize_numeric_lu_scratch::<T>(
							Par::Seq,
							Default::default(),
						),
					)),
					Default::default(),
				)
				.unwrap();
			{
				let mut x = rhs.clone();
				lu.solve_in_place_with_conj(
					crate::Conj::No,
					x.as_mut(),
					Par::Seq,
					MemStack::new(&mut MemBuffer::new(
						symbolic
							.solve_in_place_scratch::<T>(rhs.ncols(), Par::Seq),
					)),
				);
				let linsolve_diff = A * &x - &rhs;
				assert!(linsolve_diff.norm_max() <= 1e-10);
			}
			{
				let mut x = rhs.clone();
				lu.solve_in_place_with_conj(
					crate::Conj::Yes,
					x.as_mut(),
					Par::Seq,
					MemStack::new(&mut MemBuffer::new(
						symbolic
							.solve_in_place_scratch::<T>(rhs.ncols(), Par::Seq),
					)),
				);
				let linsolve_diff = A.conjugate() * &x - &rhs;
				assert!(linsolve_diff.norm_max() <= 1e-10);
			}
			{
				let mut x = rhs.clone();
				lu.solve_transpose_in_place_with_conj(
					crate::Conj::No,
					x.as_mut(),
					Par::Seq,
					MemStack::new(&mut MemBuffer::new(
						symbolic.solve_transpose_in_place_scratch::<T>(
							rhs.ncols(),
							Par::Seq,
						),
					)),
				);
				let linsolve_diff = A.transpose() * &x - &rhs;
				assert!(linsolve_diff.norm_max() <= 1e-10);
			}
			{
				let mut x = rhs.clone();
				lu.solve_transpose_in_place_with_conj(
					crate::Conj::Yes,
					x.as_mut(),
					Par::Seq,
					MemStack::new(&mut MemBuffer::new(
						symbolic.solve_transpose_in_place_scratch::<T>(
							rhs.ncols(),
							Par::Seq,
						),
					)),
				);
				let linsolve_diff = A.adjoint() * &x - &rhs;
				assert!(linsolve_diff.norm_max() <= 1e-10);
			}
		}
	}
}