view src/samer/maths/opt/UnconstrainedMinimiser.java @ 8:5e3cbbf173aa tip

Reorganise some more

/*
 *	Copyright (c) 2000, Samer Abdallah, King's College London.
 *	All rights reserved.
 *
 *	This software is provided AS iS and WITHOUT ANY WARRANTY; 
 *	without even the implied warranty of MERCHANTABILITY or 
 *	FITNESS FOR A PARTICULAR PURPOSE.
 */

package	samer.maths.opt;
import  samer.maths.*;
import  samer.core.*;
import  samer.core.util.heavy.*;


/**
		unconstrained minimiser for smooth functions:
		-	ConjugateGradient 
		-	OR Quasi-newton (using GillMurray uydates)

		-	Safeguarded cubic interpolation line search using gradients
   */





public class UnconstrainedMinimiser extends MinimiserBase
{
	GillMurray		dir;
	double			guess;

	/** expects to find Vec at top of stack, 
		Functionx just below.
	  */
	public UnconstrainedMinimiser(Vec v, Functionx f) 
	{
		// can we rely on evaluation order here?
		super(v,f);
		dir = new GillMurray(this);
		add( dir.hessin.viewable());
	}

	public void run()
	{
		double	beta;
		int		i, maxiter=getMaxiter();
		// boolean	triedSteepest=false;

		// dir.resetHessian(1);
		evaluate(); 
		dir.init();
		setSlope();
		// beta = beta.value;
		beta = initialStep();
		sig1.off();

		for (i=1; i<=maxiter; i++) {
		
			lstest.init();
			step(beta);
			ls.run(lstest);			// line search
			lsiters.next(lstest.count);
			steplength.next(alpha);
			
			if (lstest.tiny && (P2.f>=P1.f)) {
				//  tiny step was no good
				sig1.on();
				sig2.off();
				if (gconv.isSatisfied(P1.g,this)) break;
				// if (triedSteepest=true) break;
				sig2.on();

				beta = this.beta.value;
				// dir.steepest();
				dir.update(); 
				dir.init(); 
				setSlope();
				// triedSteepest=true;
				continue;
			}

			if (xfconv.isSatisfied(this)) { break; }
			
			// beta = beta.value; 
			beta = nextStep();
			dir.update();			
			move();
			perIteration();
		}

		perOptimisation(i);
		dir.hessin.changed();
	}
}