GNU Scientific Library – Reference Manual: Fitting regularized linear regression example 1

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#### 38.8.3 Regularized Linear Regression Example 1

The next program demonstrates the difference between ordinary and regularized least squares when the design matrix is near-singular. In this program, we generate two random normally distributed variables u and v, with v = u + noise so that u and v are nearly colinear. We then set a third dependent variable y = u + v + noise and solve for the coefficients c_1,c_2 of the model Y(c_1,c_2) = c_1 u + c_2 v. Since u \approx v, the design matrix X is nearly singular, leading to unstable ordinary least squares solutions.

Here is the program output:

    matrix condition number = 1.025113e+04
    === Unregularized fit ===
    best fit: y = -43.6588 u + 45.6636 v
    residual norm = 31.6248
    solution norm = 63.1764
    chisq/dof = 1.00213
    === Regularized fit (L-curve) ===
    optimal lambda: 4.51103
    best fit: y = 1.00113 u + 1.0032 v
    residual norm = 31.6547
    solution norm = 1.41728
    chisq/dof = 1.04499
    === Regularized fit (GCV) ===
    optimal lambda: 0.0232029
    best fit: y = -19.8367 u + 21.8417 v
    residual norm = 31.6332
    solution norm = 29.5051
    chisq/dof = 1.00314

We see that the ordinary least squares solution is completely wrong, while the L-curve regularized method with the optimal \lambda = 4.51103 finds the correct solution c_1 \approx c_2 \approx 1. The GCV regularized method finds a regularization parameter \lambda = 0.0232029 which is too small to give an accurate solution, although it performs better than OLS. The L-curve and its computed corner, as well as the GCV curve and its minimum are plotted below.

The program is given below.

    #include <gsl/gsl_math.h>
    #include <gsl/gsl_vector.h>
    #include <gsl/gsl_matrix.h>
    #include <gsl/gsl_rng.h>
    #include <gsl/gsl_randist.h>
    #include <gsl/gsl_multifit.h>

    int
    main()
    {
      const size_t n = 1000; /* number of observations */
      const size_t p = 2;    /* number of model parameters */
      size_t i;
      gsl_rng *r = gsl_rng_alloc(gsl_rng_default);
      gsl_matrix *X = gsl_matrix_alloc(n, p);
      gsl_vector *y = gsl_vector_alloc(n);

      for (i = 0; i < n; ++i)
        {
          /* generate first random variable u */
          double ui = 5.0 * gsl_ran_gaussian(r, 1.0);

          /* set v = u + noise */
          double vi = ui + gsl_ran_gaussian(r, 0.001);

          /* set y = u + v + noise */
          double yi = ui + vi + gsl_ran_gaussian(r, 1.0);

          /* since u =~ v, the matrix X is ill-conditioned */
          gsl_matrix_set(X, i, 0, ui);
          gsl_matrix_set(X, i, 1, vi);

          /* rhs vector */
          gsl_vector_set(y, i, yi);
        }

      {
        const size_t npoints = 200;                   /* number of points on L-curve and GCV curve */
        gsl_multifit_linear_workspace *w =
          gsl_multifit_linear_alloc(n, p);
        gsl_vector *c = gsl_vector_alloc(p);          /* OLS solution */
        gsl_vector *c_lcurve = gsl_vector_alloc(p);   /* regularized solution (L-curve) */
        gsl_vector *c_gcv = gsl_vector_alloc(p);      /* regularized solution (GCV) */
        gsl_vector *reg_param = gsl_vector_alloc(npoints);
        gsl_vector *rho = gsl_vector_alloc(npoints);  /* residual norms */
        gsl_vector *eta = gsl_vector_alloc(npoints);  /* solution norms */
        gsl_vector *G = gsl_vector_alloc(npoints);    /* GCV function values */
        double lambda_l;                              /* optimal regularization parameter (L-curve) */
        double lambda_gcv;                            /* optimal regularization parameter (GCV) */
        double G_gcv;                                 /* G(lambda_gcv) */
        size_t reg_idx;                               /* index of optimal lambda */
        double rcond;                                 /* reciprocal condition number of X */
        double chisq, rnorm, snorm;

        /* compute SVD of X */
        gsl_multifit_linear_svd(X, w);

        rcond = gsl_multifit_linear_rcond(w);
        fprintf(stderr, "matrix condition number = %e\n", 1.0 / rcond);

        /* unregularized (standard) least squares fit, lambda = 0 */
        gsl_multifit_linear_solve(0.0, X, y, c, &rnorm, &snorm, w);
        chisq = pow(rnorm, 2.0);

        fprintf(stderr, "=== Unregularized fit ===\n");
        fprintf(stderr, "best fit: y = %g u + %g v\n",
          gsl_vector_get(c, 0), gsl_vector_get(c, 1));
        fprintf(stderr, "residual norm = %g\n", rnorm);
        fprintf(stderr, "solution norm = %g\n", snorm);
        fprintf(stderr, "chisq/dof = %g\n", chisq / (n - p));

        /* calculate L-curve and find its corner */
        gsl_multifit_linear_lcurve(y, reg_param, rho, eta, w);
        gsl_multifit_linear_lcorner(rho, eta, &reg_idx);

        /* store optimal regularization parameter */
        lambda_l = gsl_vector_get(reg_param, reg_idx);

        /* regularize with lambda_l */
        gsl_multifit_linear_solve(lambda_l, X, y, c_lcurve, &rnorm, &snorm, w);
        chisq = pow(rnorm, 2.0) + pow(lambda_l * snorm, 2.0);

        fprintf(stderr, "=== Regularized fit (L-curve) ===\n");
        fprintf(stderr, "optimal lambda: %g\n", lambda_l);
        fprintf(stderr, "best fit: y = %g u + %g v\n",
                gsl_vector_get(c_lcurve, 0), gsl_vector_get(c_lcurve, 1));
        fprintf(stderr, "residual norm = %g\n", rnorm);
        fprintf(stderr, "solution norm = %g\n", snorm);
        fprintf(stderr, "chisq/dof = %g\n", chisq / (n - p));

        /* calculate GCV curve and find its minimum */
        gsl_multifit_linear_gcv(y, reg_param, G, &lambda_gcv, &G_gcv, w);

        /* regularize with lambda_gcv */
        gsl_multifit_linear_solve(lambda_gcv, X, y, c_gcv, &rnorm, &snorm, w);
        chisq = pow(rnorm, 2.0) + pow(lambda_gcv * snorm, 2.0);

        fprintf(stderr, "=== Regularized fit (GCV) ===\n");
        fprintf(stderr, "optimal lambda: %g\n", lambda_gcv);
        fprintf(stderr, "best fit: y = %g u + %g v\n",
                gsl_vector_get(c_gcv, 0), gsl_vector_get(c_gcv, 1));
        fprintf(stderr, "residual norm = %g\n", rnorm);
        fprintf(stderr, "solution norm = %g\n", snorm);
        fprintf(stderr, "chisq/dof = %g\n", chisq / (n - p));

        /* output L-curve and GCV curve */
        for (i = 0; i < npoints; ++i)
          {
            printf("%e %e %e %e\n",
                   gsl_vector_get(reg_param, i),
                   gsl_vector_get(rho, i),
                   gsl_vector_get(eta, i),
                   gsl_vector_get(G, i));
          }

        /* output L-curve corner point */
        printf("\n\n%f %f\n",
               gsl_vector_get(rho, reg_idx),
               gsl_vector_get(eta, reg_idx));

        /* output GCV curve corner minimum */
        printf("\n\n%e %e\n",
               lambda_gcv,
               G_gcv);

        gsl_multifit_linear_free(w);
        gsl_vector_free(c);
        gsl_vector_free(c_lcurve);
        gsl_vector_free(reg_param);
        gsl_vector_free(rho);
        gsl_vector_free(eta);
        gsl_vector_free(G);
      }

      gsl_rng_free(r);
      gsl_matrix_free(X);
      gsl_vector_free(y);

      return 0;
    }

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