# Nullspace¶

echelonNullspace(S, side, tol)[source]

Returns the Echelon form of the: left nullspace, $$L S = [-L0\ I] P S = 0$$ or right nullspace $$S L = S ([-L0\ I] P)^T = 0$$.

Usage

[L0, I, P, L, p] = echelonNullspace(S, side, tol)

Input

• Sm x n stoichiometric matrix

Optional inputs

• side – {‘left’, ‘right’} left or right nullspace, left by default.
• tol – upper bound on tolerance of linear independence default no greater than 1e-12

Outputs

• if side == left
• L0(m-r) x r matrix which forms the non-trivial part of the left nullspace in echelon form i.e. $$[-L0\ I] P S = 0$$.
• Pm x m row (permutation matrix
• I(m-r) x (m-r) identity matrix
• p – row permutation which leaves first 1:rankA rows independent and last rows dependent
• if side == right
• L0(n-r) x r matrix which forms the non-trivial part of the right nullspace in echelon form i.e. $$S ([-L0\ I] P)^T = 0$$.
• Pn x n column permutaion matrix
• I(n-r) x (n-r) identity matrix
• p – column permutation which leaves first 1:rankA columns independent and last columns dependent

See: Conservation analysis of large biochemical networks Ravishankar Rao Vallabhajosyula , Vijay Chickarmane and Herbert M. Sauro

getNullSpace(S, printLevel)[source]

Calculates the nullspace of S for full(S) or $$rank(S) == m$$. If full row rank i.e. rank(S) = m, it’s much faster to work with a sparse LU.

Usage

[Z, rankS] = getNullSpace(S, printLevel)

Input

• Sm x n stoichiometric matrix

Optional input

• printLevel – {0, (1)}, 0 means quiet

Outputs

• Z – (right) null space of S (when $$m \leq n$$, otherwise [])
• rankS – scalar giving rank of S
nullSpaceOperator(S, scale, printLevel)[source]

Uses LUSOL to compute a nullspace operator nullS We assume S is m x n with $$m < n$$ with rank r. First nullS = nullSpaceOperator(S) computes a structure nullS from an m x n sparse matrix S ($$m < n$$). Second, if V is an (n-r) x k sparse matrix $$(k \geq 1)$$, W = nullSpaceOperatorApply(nullS, V); computes an n x k sparse matrix W from V such that $$S W = 0$$.

This is an operator form of finding an n x (n-r) matrix Z such that $$S Z = 0$$ and then computing $$W = Z V$$. The aim is to obtain W without forming Z explicitly.

nullS.rank returns the rank of S $$(r \leq m)$$. It doesn’t matter if $$rank < m$$.

Usage

nullS = nullSpaceOperator(S, scale, printLevel)

Inputs

• Sm x n matrix
• scale – {(1), 0} geometric mean scaling of S
• printLevel – {(1), 0}

Output

• nullS – nullspace operator to be used with nullSpaceOperatorApply.m
• nullS.rank - rank of S

Note

Requires Nick Henderson’s 64 bit LUSOL interface to be intalled and added to the matlab path. See https://github.com/nwh/lusol see also https://web.stanford.edu/group/SOL/software/lusol/

nullSpaceOperatorApply(nullS, V)[source]

Computes a sparse matrix W from V such that $$S W = 0$$. First, nullS = nullSpaceOperator(S) computes a structure nullS from an m x n sparse matrix S $$(m < n)$$, with rank r.

Second, if V is an (n-r) x k sparse matrix $$(k \geq 1)$$, W = nullSpaceOperatorApply(nullS, V); computes an n x k sparse matrix W from V such that $$S W = 0$$.

This is an operator form of finding an n x (n-r) matrix Z such that $$S Z = 0$$ and then computing $$W = Z V$$. The aim is to obtain W without forming Z explicitly.

nullspaceLUSOLapply2Modes(mode, m, n, V, nullS)[source]

Computes the matrix vector product with the operator nullspace function handle of the form y = pdMat(mode, m, n, x)

Usage

W = nullspaceLUSOLapply2Modes(mode, m, n, V, nullS)

Inputs

• mode$$mode = 1$$ returns $$W = Z V$$, mode = 2 returns $$W = Z^T V$$
• m – first dimension of the matrix
• n – second dimension of the matrix
• V – one of the components of the multiplication
• nullS – structure nullS from the function nullspaceLUSOLform(S); where m x n sparse matrix S ($$m < n$$).

Output

• W – Matrix vector product