function exam2017MajUppg8()
  
clear all
close all

%% only visualization
fXY = @(x,y) y.^2/2-x+x.^2.*(x+y).^2/4; %defined with (x_1,x_2) = (x,y) so we can use it in the contour plot.
[X,Y] = meshgrid(-0.5:0.005:2,-2:0.005:1);
Z = fXY(X,Y);
[C,h] = contour(X,Y,Z,-0.9157 + logspace(-5,1,40));
xlabel('x')
ylabel('y')
hold on

%% Algorithm
f = @(x) x(2).^2/2-x(1)+x(1).^2*(x(1)+x(2))^2/4;
gradF = @(x)[-1+(x(1)*(x(1)+x(2))^2+x(1)^2*(x(1)+x(2)))/2; x(2)+x(1)^2*(x(1)+x(2))/2];

% Need hessian to solve line search minimization
H = @(x)[3*x(1)^2+3*x(1)*x(2)+x(2)^2/2  3*x(1)^2/2+x(1)^2*x(2); 
    3*x(1)^2/2+x(1)*x(2) 1+x(1)^2/2];

x{1} = [0;0];
plot([x{1}(1)], [x{1}(2)], 'ro','linewidth',3)
pause(1.5)

iter = 10; xVec = x{1}; fVec = f(x{1}); alphaVec = []; sVec = []; % output variables not important for algorithm
for k =1:iter
    % steepest descent sokriktning
    s{k} = -gradF(x{k});
        
    % Newtons metod losning av linjesokningsproblem: min_alpha_k  f(x{k} + alpha_k s{k})
    %vi anvander 5 iterationer
    alpha{k} = 1;
    for j=1:5
        alpha{k} = alpha{k} ...
            - gradF(x{k}+alpha{k}*s{k})'*s{k}/(s{k}'*H(x{k}+alpha{k}*s{k})*s{k});
    end
    
    % Uppdatering steepest descent
    x{k+1} = x{k} +alpha{k}*s{k};
    plot([x{k}(1) x{k+1}(1)], [x{k}(2) x{k+1}(2)], 'r-o','linewidth',2)
    pause(1.5)
    sVec = [sVec s{k}];
    alphaVec = [alphaVec alpha{k}];
    xVec = [xVec x{k+1}];
    fVec = [fVec f(x{k+1})];
end


xVec  
sVec
alphaVec
fVec


[xRef,fMinValRef] = fminsearch(f,[0;0])%matlabs minimeringsfunktion [0;0] är startapproximation.

end