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lbfgs.c  view on Meta::CPAN

		/* Report the progress. */
		if (proc_progress) {
			if (ret = proc_progress(instance, x, g, fx, xnorm, gnorm, step, n, k, ls)) {
				goto lbfgs_exit;
			}
		}

		/*
			Convergence test.
			The criterion is given by the following formula:
				|g(x)| / \max(1, |x|) < \epsilon
		 */
		if (xnorm < 1.0) xnorm = 1.0;
		if (gnorm / xnorm <= param->epsilon) {
			/* Convergence. */
			ret = 0;
			break;
		}

		if (param->max_iterations != 0 && param->max_iterations < k+1) {

lbfgs.c  view on Meta::CPAN

			Compute scalars ys and yy:
				ys = y^t \cdot s = 1 / \rho.
				yy = y^t \cdot y.
			Notice that yy is used for scaling the hessian matrix H_0 (Cholesky factor).
		 */
		vecdot(&ys, it->y, it->s, n);
		vecdot(&yy, it->y, it->y, n);
		it->ys = ys;

		/*
			Recursive formula to compute dir = -(H \cdot g).
				This is described in page 779 of:
				Jorge Nocedal.
				Updating Quasi-Newton Matrices with Limited Storage.
				Mathematics of Computation, Vol. 35, No. 151,
				pp. 773--782, 1980.
		 */
		bound = (m <= k) ? m : k;
		++k;
		end = (end + 1) % m;

lbfgs.h  view on Meta::CPAN

In addition, a user must preserve two requirements:
	- The number of variables must be multiples of 16 (this is not 4).
	- The memory block of variable array ::x must be aligned to 16.

This algorithm terminates an optimization
when:

	||G|| < \epsilon \cdot \max(1, ||x||) .

In this formula, ||.|| denotes the Euclidean norm.
*/

/**
 * Start a L-BFGS optimization.
 *
 *	@param	n			The number of variables.
 *	@param	x			The array of variables. A client program can set
 *						default values for the optimization and receive the
 *						optimization result through this array.
 *	@param	ptr_fx		The pointer to the variable that receives the final