comparison src/samer/maths/opt/ConstrainedMinimiser.java @ 0:bf79fb79ee13

Initial Mercurial check in.

+/*
+ *	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.types.*;
+import  samer.core.util.heavy.*;
+
+/**
+		Constrained minimiser.
+
+		-	ConjugateGradient 
+		-	OR Quasi-newton (using GillMurray uydates)
+		-	Safeguarded cubic interpolation line search using gradients
+
+		We expect a Class object to be in object Space
+		to tell us what kind of constraints to create
+  */
+
+
+public class ConstrainedMinimiser extends MinimiserBase implements Agent
+{
+	ConstrainedGillMurray	dir;
+//	ConstrainedConjGrad		dir;
+	Constraints					cons;	// handles line search
+	VDouble						percentage;
+	boolean toggle=false;
+
+	/** expected stack top: Vec, Functionx, Class */
+	public ConstrainedMinimiser(Vec v, Functionx f, Constraints.Factory constraintFactory) 
+	{
+		super(v,f);
+		cons = constraintFactory.build(this);
+		init();
+	}
+
+	public ConstrainedMinimiser(Vec v, Functionx f, Class conscl)
+	{
+		super(v,f);
+
+		if (conscl==ZeroCrossingSparsity.class) {
+			cons = new ZeroCrossingSparsity(this);
+		} else if (conscl==Positivity.class) {
+			cons = new Positivity(this);
+		} else {
+			throw new Error("Unrecognised constrain class: "+conscl);
+		}
+		init();
+	}
+
+	private void init() {
+		dir = new ConstrainedGillMurray(this,cons);
+//		dir = new ConstrainedConjGrad(this,cons);
+		percentage=new VDouble("releaseFactor",0.01);
+
+		// add( dir.hessin.viewable());
+		add( percentage);
+		cons.activateAll();
+	}
+
+	public void setHessian(Jama.Matrix H) { dir.setInitialHessian(H); }
+
+	public void getCommands(Agent.Registry r) {
+		super.getCommands(r);
+		r.add("all").add("run");
+		r.add("report").add("release").add("resetHess");
+	}
+
+	public void execute(String cmd, Environment env) throws Exception
+	{
+		if (cmd.equals("run"))  			run();
+		else if (cmd.equals("report")) 	cons.report();
+		else if (cmd.equals("release")) 	releaseDimensions(P1);
+		else if (cmd.equals("all")) {
+			toggle = X._bool(env.datum(),!toggle);
+			if (toggle) cons.activateAll();
+			else cons.deactivateAll();
+		} else if (cmd.equals("resetHess")) {
+			dir.resetHessian();
+		} else super.execute(cmd,env);
+	}
+
+	private boolean releaseDimensions(Datum P)
+	{
+		if (cons.release(P)) {
+			dir.resetDimension(cons.getReleasedDimension());
+
+			int p=cons.getReleasableDimensions()-1;
+
+			// going to release a percentage of releasable dims?
+			// or absolute number?
+			p=(int)(percentage.value*p);
+			// if (p>1) p=1;
+			for (; p>0; p--) {
+				cons.release(P);
+				dir.resetDimension(cons.getReleasedDimension());
+			}
+
+ 			return true;
+		}
+		return false;
+	}
+
+	public void run()
+	{
+		double	beta;
+		int		i, maxiter=getMaxiter();
+		boolean	triedSteepest=false;
+
+		// we start from the current state instead.
+		// cons.initialise should set up the constraints
+		// to reflect the current state.
+		cons.initialise();
+
+		evaluate();
+		releaseDimensions(P1);
+
+		if (cons.activeCount()==0) {
+			iters.next(0);
+			sig3.off();
+			return;
+		}
+
+
+		sig1.off();
+
+		// must reset hessian for active dimensions
+		cons.zeroInactive(P2.x);
+		dir.resetHessian();
+		dir.hessin.changed();
+		dir.init();
+		setSlope();
+		beta = initialStep();
+		// beta = beta.value; ??
+
+		for (i=0; i<maxiter; i++) {
+
+			// constrained line search with initial step beta0
+			lstest.init();
+			cons.lineSearch(lstest,ls,beta);
+			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,cons)) break;
+				if (triedSteepest) break;
+				sig2.on();
+
+				beta = this.beta.value;
+				dir.update();			// ??
+				if (dir.uvc) dir.init();
+				else		 dir.steepest();
+				setSlope();
+				triedSteepest=true;
+				continue;
+			}
+
+
+			// check gradients at P2
+			if (!releaseDimensions(P2)) {
+				// don't quit if any dimensions released
+				if (cons.activeCount()==0) break;
+				if (xfconv.isSatisfied(this)) {
+					if (gconv.isSatisfied(P2.g,cons)) break;
+				}
+			}
+
+			// beta = beta.value;
+			beta = nextStep();
+			dir.update();
+			move();
+			perIteration();
+		}
+
+		perOptimisation(i);
+		dir.hessin.changed();
+	}
+}
+
+
+
+