{
pwr = 1.5;
quadS = "{expr2 = (x-y+1); Value = expr2^pwr + x^pwr + 10.; expr7 = pwr-1.; expr9= (expr2^expr7)*pwr; Gradient = new double[2]; Gradient[[0]] =expr9 + ((x^expr7) * pwr); Gradient[[1]] = -expr9;}";

// function produced from S function derivOmegahat (on merced)
quadS = function(x, y){
	Value = new Object[2];
	grad=new double[ 2 ];
	 expr2 = (x - y) + 1; 
	 expr3 = expr2^2; 
	 expr5 = x^2; 
	 expr8 = pwr - 1.; 
	 expr12 = (expr3^expr8) * (pwr * (2 * expr2)); 
	 value = (expr3^pwr) + (expr5^pwr) + 10.; 
	 grad[[0]] = expr12 + ((expr5^expr8) * (pwr * (2 * x))); 
	 grad[[1]] =  - expr12; 
	Value[[0]] = value; Value[[1]] = grad;
	return(Value);
};

quadE = parse("{GV=quadS(x,y); Value = GV[[0]]; Gradient=GV[[1]];}");

quadM = new ModelObjectiveOmegahat(quadE);

quadM.setEvaluator(this);

xy = new double[] {.5, .5};


xyNames = new String[]{"x", "y"};

quadM.setParameterNames(xyNames);
opta = new OptimizerAlgorithmBFGS();
// opt = new Optimizer(opta, quadM, new doubleArray(xy));

xp = opta.newModelPoint();
xp.getParameters().setData(xy);
xp.setModel(quadM);

opt = new Optimizer(opta, xp);

dynamicClassLoader().defineClass("org.omegahat.Numerics.Optimizers.InformationListener", "FL");
xyf = function(ev){show(x); show(y);};
ll = new FL(xyf, this);
opt.addListener(ll);
pl = new PlotListener();
pl.make(xp, opt);
}