#include #include #include #include #include #include #include #include /* function to be fitted */ double func(const double t) { double x = sin(10.0 * t); return exp(x*x*x); } /* construct a row of the least squares matrix */ int build_row(const double t, gsl_vector *row) { const size_t p = row->size; double Xj = 1.0; size_t j; for (j = 0; j < p; ++j) { gsl_vector_set(row, j, Xj); Xj *= t; } return 0; } int solve_system(const int print_data, const gsl_multilarge_linear_type * T, const double lambda, const size_t n, const size_t p, gsl_vector * c) { const size_t nblock = 5; /* number of blocks to accumulate */ const size_t nrows = n / nblock; /* number of rows per block */ gsl_multilarge_linear_workspace * w = gsl_multilarge_linear_alloc(T, p); gsl_matrix *X = gsl_matrix_alloc(nrows, p); gsl_vector *y = gsl_vector_alloc(nrows); gsl_rng *r = gsl_rng_alloc(gsl_rng_default); const size_t nlcurve = 200; gsl_vector *reg_param = gsl_vector_alloc(nlcurve); gsl_vector *rho = gsl_vector_alloc(nlcurve); gsl_vector *eta = gsl_vector_alloc(nlcurve); size_t rowidx = 0; double rnorm, snorm, rcond; double t = 0.0; double dt = 1.0 / (n - 1.0); while (rowidx < n) { size_t nleft = n - rowidx; /* number of rows left to accumulate */ size_t nr = GSL_MIN(nrows, nleft); /* number of rows in this block */ gsl_matrix_view Xv = gsl_matrix_submatrix(X, 0, 0, nr, p); gsl_vector_view yv = gsl_vector_subvector(y, 0, nr); size_t i; /* build (X,y) block with 'nr' rows */ for (i = 0; i < nr; ++i) { gsl_vector_view row = gsl_matrix_row(&Xv.matrix, i); double fi = func(t); double ei = gsl_ran_gaussian (r, 0.1 * fi); /* noise */ double yi = fi + ei; /* construct this row of LS matrix */ build_row(t, &row.vector); /* set right hand side value with added noise */ gsl_vector_set(&yv.vector, i, yi); if (print_data && (i % 100 == 0)) printf("%f %f\n", t, yi); t += dt; } /* accumulate (X,y) block into LS system */ gsl_multilarge_linear_accumulate(&Xv.matrix, &yv.vector, w); rowidx += nr; } if (print_data) printf("\n\n"); /* compute L-curve */ gsl_multilarge_linear_lcurve(reg_param, rho, eta, w); /* solve large LS system and store solution in c */ gsl_multilarge_linear_solve(lambda, c, &rnorm, &snorm, w); /* compute reciprocal condition number */ gsl_multilarge_linear_rcond(&rcond, w); fprintf(stderr, "=== Method %s ===\n", gsl_multilarge_linear_name(w)); fprintf(stderr, "condition number = %e\n", 1.0 / rcond); fprintf(stderr, "residual norm = %e\n", rnorm); fprintf(stderr, "solution norm = %e\n", snorm); /* output L-curve */ { size_t i; for (i = 0; i < nlcurve; ++i) { printf("%.12e %.12e %.12e\n", gsl_vector_get(reg_param, i), gsl_vector_get(rho, i), gsl_vector_get(eta, i)); } printf("\n\n"); } gsl_matrix_free(X); gsl_vector_free(y); gsl_multilarge_linear_free(w); gsl_rng_free(r); gsl_vector_free(reg_param); gsl_vector_free(rho); gsl_vector_free(eta); return 0; } int main(int argc, char *argv[]) { const size_t n = 50000; /* number of observations */ const size_t p = 16; /* polynomial order + 1 */ double lambda = 0.0; /* regularization parameter */ gsl_vector *c_tsqr = gsl_vector_alloc(p); gsl_vector *c_normal = gsl_vector_alloc(p); if (argc > 1) lambda = atof(argv[1]); /* solve system with TSQR method */ solve_system(1, gsl_multilarge_linear_tsqr, lambda, n, p, c_tsqr); /* solve system with Normal equations method */ solve_system(0, gsl_multilarge_linear_normal, lambda, n, p, c_normal); /* output solutions */ { gsl_vector *v = gsl_vector_alloc(p); double t; for (t = 0.0; t <= 1.0; t += 0.01) { double f_exact = func(t); double f_tsqr, f_normal; build_row(t, v); gsl_blas_ddot(v, c_tsqr, &f_tsqr); gsl_blas_ddot(v, c_normal, &f_normal); printf("%f %e %e %e\n", t, f_exact, f_tsqr, f_normal); } gsl_vector_free(v); } gsl_vector_free(c_tsqr); gsl_vector_free(c_normal); return 0; }