Biconjugate gradient method

In mathematics, more specifically in numerical analysis, the biconjugate gradient method is an algorithm to solve systems of linear equations

A x= b.,

Unlike the conjugate gradient method, this algorithm does not require the matrix A to be self-adjoint, but instead one needs to perform multiplications by the conjugate transpose A^*.

The algorithm

  1. Choose x_0, y_0, a regular preconditioner M (frequently M^{-1}=1 is used) and c;
  2. r_0 leftarrow b-A x_0, s_0 leftarrow c-A^* y_0,;
  3. d_0 leftarrow M^{-1} r_0, f_0 leftarrow M^{-*} s_0,;
  4. for k=0, 1, dots, do
  5. alpha_k leftarrow {s^*_k M^{-1} r_k over f_k^* A d_k},;
  6. x_{k+1} leftarrow x_k+ alpha_k d_k, left(y_{k+1} leftarrow y_k + overline{alpha_k} f_k right),;
  7. r_{k+1} leftarrow r_k- alpha_k A d_k ,, s_{k+1} leftarrow s_k- overline{alpha_k} A^* f_k , (r_k=b-A x_k and s_k= c- A^* y_k are the residuums);
  8. beta_k leftarrow {s_{k+1}^* M^{-1} r_{k+1} over s^*_k M^{-1} r_k},;
  9. d_{k+1} leftarrow M^{-1} r_{k+1} + beta_k d_k,, f_{k+1} leftarrow M^{-*} s_{k+1} + overline{beta_k} f_k,.


The BiCG method is numerically unstable, but very important from theoretical point of view: Define the iteration steps by x_k:=x_j+ P_k A^{-1}left(b - A x_j right) and y_k:=y_j+A^{-*}P_k^*left(c-A^* y_j right) (j) using the related projection
P_k:= mathbf{u_k} left(mathbf{v_k}^* A mathbf{u_k} right)^{-1} mathbf{v_k}^* A,
where mathbf{u_k}=left(u_0, u_1, dots u_{k-1} right) and mathbf{v_k}=left(v_0, v_1, dots v_{k-1} right). These related projections may be iterated themselves, as
P_{k+1}= P_k+ left(1-P_kright) u_k otimes {v_k^* Aleft(1-P_k right) over v_k^* Aleft(1-P_k right) u_k}.

The new directions d_k:= left(1-P_k right) u_k and f_k:= left(A left(1- P_k right) A^{-1} right)^* v_k then are orthogonal to the residuums: v_i^* r_k= f_i^* r_k=0 and s_k^* u_j = s_k^* d_j= 0, which themselves satisfy r_k= A left(1- P_k right) A^{-1} r_j and s_k= left(1- P_k right) ^* s_j(i,j).

The biconjugate gradient method now makes a special choice and uses the setting

u_k:= M^{-1} r_k and v_k:=M^{-*} s_k.
This special choice allows to avoid direct evaluations of P_k and A^{-1}, and subsequently leads to the algorithm as stated above.


  • If A= A^* is self-adjoint, y_0= x_0 and c=b, then r_k= s_k, d_k= f_k, and the conjugate gradient method produces the same sequence x_k= y_k.
  • In finite dimensions x_n=A^{-1}b, at the latest when P_n=1: The biconjugate gradient method returns the exact solution after iterating the full space and thus is a direct method.
  • The sequences produced by the algorithm are biorthogonal: f_i^* A d_j = 0 and s_i^* M^{-1} r_j=0 for i ne j.
  • if p_{j'} is a polynomial with degleft(p_{j'} right) +j , then s_k^* p_{j'}left(M^{-1} A right) u_j=0. The algorithm thus produces projections onto the Krylov subspace;
  • if p_{i'} is a polynomial with i+degleft(p_{i'} right) , then v_i^* p_{i'}left(A M^{-1} right) r_k=0.

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