Uzawa iteration

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In numerical mathematics, the Uzawa iteration is an algorithm for solving saddle point problems. It is named after Hirofumi Uzawa and was originally introduced in the context of concave programming.[1]

Basic idea[edit]

We consider a saddle point problem of the form

 \begin{pmatrix} A & B\\ B^* & \end{pmatrix} \begin{pmatrix} x_1\\ x_2 \end{pmatrix}
         = \begin{pmatrix} b_1\\ b_2 \end{pmatrix},

where A is a symmetric positive-definite matrix. Multiplying the first row by B^* A^{-1} and subtracting from the second row yields the upper-triangular system

 \begin{pmatrix} A & B\\ & -S \end{pmatrix} \begin{pmatrix} x_1\\ x_2 \end{pmatrix}
         = \begin{pmatrix} b_1\\ b_2 - B^* A^{-1} b_1 \end{pmatrix},

where S := B^* A^{-1} B denotes the Schur complement. Since S is symmetric positive-definite, we can apply standard iterative methods like the gradient descent method or the conjugate gradient method to

 S x_2 = B^* A^{-1} b_1 - b_2

in order to compute x_2. The vector x_1 can be reconstructed by solving

 A x_1 = b_1 - B x_2. \,

It is possible to update x_1 alongside x_2 during the iteration for the Schur complement system and thus obtain an efficient algorithm.


We start the conjugate gradient iteration by computing the residual

 r_2 := B^* A^{-1} b_1 - b_2 - S x_2 = B^* A^{-1} (b_1 - B x_2) - b_2 = B^* x_1 - b_2,

of the Schur complement system, where

 x_1 := A^{-1} (b_1 - B x_2)

denotes the upper half of the solution vector matching the initial guess x_2 for its lower half. We complete the initialization by choosing the first search direction

 p_2 := r_2.\,

In each step, we compute

 a_2 := S p_2 = B^* A^{-1} B p_2 = B^* p_1

and keep the intermediate result

 p_1 := A^{-1} B p_2

for later. The scaling factor is given by

 \alpha := p_2^* r_2 / p_2^* a_2

and leads to the updates

 x_2 := x_2 + \alpha p_2, \quad r_2 := r_2 - \alpha a_2.

Using the intermediate result p_1 saved earlier, we can also update the upper part of the solution vector

 x_1 := x_1 - \alpha p_1.\,

Now we only have to construct the new search direction by the Gram–Schmidt process, i.e.,

 \beta := r_2^* a_2 / p_2^* a_2,\quad p_2 := r_2 - \beta p_2.

The iteration terminates if the residual r_2 has become sufficiently small or if the norm of p_2 is significantly smaller than r_2 indicating that the Krylov subspace has been almost exhausted.

Modifications and extensions[edit]

If solving the linear system A x=b exactly is not feasible, inexact solvers can be applied.[2][3][4]

If the Schur complement system is ill-conditioned, preconditioners can be employed to improve the speed of convergence of the underlying gradient method.[2][5]

Inequality constraints can be incorporated, e.g., in order to handle obstacle problems.[5]


  1. ^ Uzawa, H. (1958). "Iterative methods for concave programming". In Arrow, K. J.; Hurwicz, L.; Uzawa, H. Studies in linear and nonlinear programming. Stanford University Press. 
  2. ^ a b Elman, H. C.; Golub, G. H. (1994). "Inexact and preconditioned Uzawa algorithms for saddle point problems". SIAM J. Num. Anal. 31 (6): 1645–1661. doi:10.1137/0731085. 
  3. ^ Bramble, J. H.; Pasciak, J. E.; Vassilev, A. T. (1997). "Analysis of the inexact Uzawa algorithm for saddle point problems". SIAM J. Num. Anal. 34 (3): 1072–1982. doi:10.1137/S0036142994273343. 
  4. ^ Zulehner, W. (1998). "Analysis of iterative methods for saddle point problems. A unified approach". Math. Comp. 71: 479–505. doi:10.1090/S0025-5718-01-01324-2. 
  5. ^ a b Gräser, C.; Kornhuber, R. (2007). "On Preconditioned Uzawa-type Iterations for a Saddle Point Problem with Inequality Constraints". Domain Decomposition Methods in Science and Engineering XVI. Lec. Not. Comp. Sci. Eng. 55. pp. 91–102. doi:10.1007/978-3-540-34469-8_8. 

Further reading[edit]