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The MINRES method iteratively calculates an approximate solution of a linear system of equations of the form =, where is a symmetric matrix and a vector. For this, the norm of the residual ():= in a -dimensional Krylov subspace = + {, …,} is minimized.
The gradient of f equals Ax − b. Starting with an initial guess x 0, this means we take p 0 = b − Ax 0. The other vectors in the basis will be conjugate to the gradient, hence the name conjugate gradient method. Note that p 0 is also the residual provided by this initial step of the algorithm. Let r k be the residual at the kth step:
These use a three-term recurrence relation, but they do not attain the minimum residual, and hence the residual does not decrease monotonically for these methods. Convergence is not even guaranteed. The third class is formed by methods like CGS and BiCGSTAB. These also work with a three-term recurrence relation (hence, without optimality) and ...
Mathematically, linear least squares is the problem of approximately solving an overdetermined system of linear equations A x = b, where b is not an element of the column space of the matrix A. The approximate solution is realized as an exact solution to A x = b' , where b' is the projection of b onto the column space of A .
For example, in the MATLAB or GNU Octave function pinv, the tolerance is taken to be t = ε⋅max(m, n)⋅max(Σ), where ε is the machine epsilon. The computational cost of this method is dominated by the cost of computing the SVD, which is several times higher than matrix–matrix multiplication, even if a state-of-the art implementation ...
Quadratic programming in MATLAB requires the Optimization Toolbox in addition to the base MATLAB product Mathematica: A general-purpose programming-language for mathematics, including symbolic and numerical capabilities. MOSEK: A solver for large scale optimization with API for several languages (C++, Java, .Net, Matlab and Python). NAG ...
Standard method like Gauss elimination can be used to solve the matrix equation for .A more numerically stable method is provided by QR decomposition method. Since the matrix is a symmetric positive definite matrix, can be solved twice as fast with the Cholesky decomposition, while for large sparse systems conjugate gradient method is more effective.
Input: initial guess x (0) to the solution, (diagonal dominant) matrix A, right-hand side vector b, convergence criterion Output: solution when convergence is reached Comments: pseudocode based on the element-based formula above k = 0 while convergence not reached do for i := 1 step until n do σ = 0 for j := 1 step until n do if j ≠ i then ...