The following example is provided as the first example of using matrices and linear algebra in numpy (hopefully others will provide more). Its intent is to provide a concrete example upon which design decisions can be made with regard to the matrix class and the linalg module.

### Conjugate Gradient

I wish to return *x*, which solves *A x = b*, where *A* is a matrix, *b* is a column vector and *x* is also a column vector. When *A* is symmetric and positive definite, then the conjugate gradient method is guaranteed to converge.

This algorithm can be expanded to handle multiple right-hand sides, producing multiple solutions. In this case, *b* and *x* become matrices (collections of column vectors). For cases where the application allows for simultaneous solutions, it can be a big win: we promote some of our linear algebra from level-2 BLAS to level-3 BLAS, which can be much more efficient. (I don't actually know if numpy uses level-3 BLAS, but I can assume here that matrix-matrix multiplication does, or someday will.)

The algorithm I came up with is as follows:

import numpy as np def col_vector_norms(a,order=None): """ Return an array representing the norms of a set of column vectors. Arguments: a - An (n x m) numpy matrix, representing m column vectors of length n. order - The order of the norm. See numpy.linalg.norm for more information. """ (nrow,ncol) = a.shape norms = np.zeros((ncol,),a.dtype) for j in range(ncol): col = a[:,j].A col.shape = (nrow,) norms[j] = np.linalg.norm(col,order) return norms def cg(A, b, x=None, tol=1.0e-8, itmax=None, info=False): """ Use the conjugate gradient method to return x such that A * x = b. Arguments: A - A square numpy matrix representing the linear operator. If A is symmetric, then this algorithm is guaranteed to converge. b - An (n x m) numpy matrix representing the right hand side, where m is the number of column vectors and n is the size of A. x - A numpy matrix, same shape as b, that serves as the initial guess for the algorithm. Defaults to the zero matrix. If given, the return matrix is this matrix. tol - Accuracy tolerance for the algorithm stopping criterion. Default 1.0e-8. itmax - Maximun allowed iterations. Default is n, the size of A. info - If info is True, return a tuple (x, k, norms) where x is the solution, k is the required iterations and norms is an array of the final residual norms. Default False. """ # Sanity checks. I check the types of A, b and x to ensure that I will get # matrix algebra. if not isinstance(A,np.matrix): raise TypeError, "A must be a matrix" (nrow,ncol) = A.shape if nrow != ncol: raise ValueError, "A must be a square matrix" if not isinstance(b,np.matrix): raise TypeError, "b must be a matrix" if x is None: x = np.matrix(np.zeros(b.shape, b.dtype)) if not isinstance(x,np.matrix): raise TypeError, "x must be a matrix" if itmax is None: itmax = nrow # Initialization nrhs = b.shape[1] r2 = np.zeros((nrhs,), b.dtype) alpha = np.zeros((nrhs,), b.dtype) beta = np.zeros((nrhs,), b.dtype) r = b - A * x p = r.copy() k = 0 # Main conjugate gradient loop while k < itmax: for j in range(nrhs): r2[j] = r[:,j].T * r[:,j] for j in range(nrhs): alpha[j] = r2[j] / (p[:,j].T * A * p[:,j]) x[:] += p.A * alpha r[:] -= (A * p).A * alpha norms = col_vector_norms(r,2) if (norms < tol).all(): break for j in range(nrhs): beta[j] = (r[:,j].T * r[:,j]) / r2[j] p[:] = r + p.A * beta k += 1 # Return the requested information if info: return (x, k, norms) else: return x

Here is essentially the same thing written for array operations. (This time both arrays and matrices are correctly handled.) Note that the matrix test in `col_vector_norms2` would not be necessary if iteration over matrices were to yield 1d arrays (as is under discussion), and that the test then reduces to a single line that could be moved into the algorithm itself:

def col_vector_norms2(a,order=None): """ See doc for col_vector_norms """ if isinstance(a,np.matrix): a = a.A norms = np.fromiter((np.linalg.norm(col,order) for col in a.T),a.dtype) return norms def cg2(A, b, x=None, tol=1.0e-8, itmax=None, info=False): """ See doc string for cg. """ matrixout = True if isinstance(A,np.matrix) else False A = np.asarray(A) b = np.asarray(b) nrow, ncol = A.shape if nrow != ncol: raise ValueError, "A must be square" if x is None: x = np.zeros(b.shape, b.dtype) if itmax is None: itmax = nrow # Initialization nrhs = b.shape[1] alpha = np.zeros((nrhs,), b.dtype) beta = np.zeros((nrhs,), b.dtype) r2old = np.zeros((nrhs,), b.dtype) r = b - np.dot(A,x) r2 = (r * r).sum(axis=0) p = r.copy() k = 0 # Main conjugate gradient loop while k < itmax: for j in range(nrhs): alpha[j] = r2[j] / (np.outer(p[:,j],p[:,j]) * A).sum() # or replace the loop by: # alpha = r2 / (p*np.dot(A,p)).sum(axis=0) x[:] += p * alpha r[:] -= np.dot(A , p) * alpha norms = col_vector_norms2(r,2) if (norms < tol).all(): break r2old[:] = r2 r2[:] = (r * r).sum(axis=0) beta = r2 / r2old p[:] = r + (p * beta) k += 1 if matrixout: x = np.asmatrix(x) # Return the requested information if info: return (x, k, norms) else: return x

### Discussion

The function cg() takes as input right-hand side b, which is passed in as a matrix. Conceptually, it and x are a collection of independent column vectors, although they need to be matrices to take advantage of level-3 BLAS.

The CG algorithm is considered converged when the norm of the residual *r = b - A x* is less than some specified tolerance. For multiple right-hand sides, we actually have an array of residual norms. Getting this right took more than just a couple of lines of code, so I broke out the computation of these norms into its own function.

This illustrates an example of where row_vector and col_vector classes can provide an advantage. I am using the numpy.linalg.norm() function, which can compute norms of both "vectors" and "matrices", which are two different operations. As near as I can tell, it checks the number of dimensions and interprets 1D arrays as vectors and 2D arrays as matrices. So I have to extract a column from my matrix, interpret it as an array and then change its shape to be 1D before calling norm(). If there were row_vector and col_vector classes, and they were naturally extracted from matrix objects, and the norm() function checked for these types before proceeding appropriately, this code would be much cleaner and readable.

I first wrote this algorithm for single vectors *b* and *x*, but expressed as *n* x 1 matrices. In that version of the algorithm, I would compute:

r2 = r.T * r

which is very readable, but r2 was a 1 x 1 matrix and I couldn't divide by it. So I had to change it to:

r2 = (r.T * r)[0,0]

which is less readable. When I expanded it to handle multiple solutions, I was able to get rid of the indexing:

r2[j] = r[:,j].T * r[:,j]

because r2 is now a numpy array and I guess the conversion from 1 x 1 matrix to scalar is handled automatically.

I'd be curious to see if anybody can come up with a way to fill up r2, alpha and beta by iterating over columns rather than iterating over column indexes.

The second version of the algorithm provided above suggests that little if anything was gained by the use of matrices.