# Section 7.1.1: Covariance estimation for Gaussian variables

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    % Boyd & Vandenberghe "Convex Optimization"
    % JoÃ«lle Skaf - 04/24/08
    %
    % Suppose y \in\reals^n is a Gaussian random variable with zero mean and
    % covariance matrix R = \Expect(yy^T). We want to estimate the covariance
    % matrix R based on N independent samples y1,...,yN drawn from the
    % distribution, and using prior knowledge about R (lower and upper bounds
    % on R)
    %           L <= R <= U
    % Let S be R^{-1}. The maximum likelihood (ML) estimate of S is found
    % by solving the problem
    %           maximize    logdet(S) - tr(SY)
    %           subject to  U^{-1} <= S <= L^{-1}
    % where Y is the sample covariance of y1,...,yN.

    % Input data
    randn('state',0);
    n = 10;
    N = 1000;
    tmp = randn(n);
    L = tmp*tmp';
    tmp = randn(n);
    U = L + tmp*tmp';
    R = (L+U)/2;
    y_sample = sqrtm(R)*randn(n,N);
    Y = cov(y_sample');
    Ui = inv(U); Ui = 0.5*(Ui+Ui');
    Li = inv(L); Li = 0.5*(Li+Li');

    % Maximum likelihood estimate of R^{-1}
    cvx_begin sdp
        variable S(n,n) symmetric
        maximize( log_det(S) - trace(S*Y) );
        S >= Ui;
        S <= Li;
    cvx_end
    R_hat = inv(S);

    Successive approximation method to be employed.
       sedumi will be called several times to refine the solution.
       Original size: 357 variables, 234 equality constraints
       1 exponentials add 8 variables, 5 equality constraints
    -----------------------------------------------------------------
     Cones  |             Errors              |
    Mov/Act | Centering  Exp cone   Poly cone | Status
    --------+---------------------------------+---------
      1/  1 | 1.981e+00  2.907e-01  9.261e-08 | Solved
      1/  1 | 9.437e-02  6.354e-04  3.371e-07 | Solved
      1/  1 | 3.752e-03  1.347e-06  3.674e-07 | Solved
      0/  1 | 1.626e-04  3.727e-07  3.698e-07 | Solved
    -----------------------------------------------------------------
    Status: Solved
    Optimal value (cvx_optval): -30.6698