1;

# LS objective function
function obj_value = model(args)
	theta = args{1};
	y = args{2};
	x = args{3};
	a = theta(1,:);	
	b = theta(2,:);
	c = theta(3,:);
	d = theta(4,:);
	errors = y - a*sin(b*x+c)-d; 
	obj_value = errors'*errors;
endfunction	

n = 100; # sample size

# generate data
x = randn(n,1);
noise = randn(n,1);
a = 1;
b = 1;
c = 1;
d = 1;

y = a*sin(b*x+c)+d + noise;
theta = ones(4,1); # start with correct values, since we know them ;>)
control = [100; 1; 1];
[theta, obj_value, iterations, convergence] = BFGSMin("model", {theta, y, x}, control);