Optimization under constraint
Here is an incredible powerfull solution of a constrained optimization that Mathematica cannot solve without help
var('A, x, y, l, alpha, beta, R, p_x, p_y');
U= A*x^(alpha)*y^(beta);
show(U)
D = p_x*x + p_y*y;
show(D)
show(U)
solve(D==R, y)
L = U-l*(D-R)
show(L)
L_x= L.diff(x)
show(L_x)
L_y= L.diff(y)
show(L_y)
L_lambda= L.diff(l)
show(L_l)
z=solve([L_x==0, L_y==0, L_l==0,], x, y, l)
Now I have two questions in one :
I would use greek lambda in place of l ?
I would retreive the values of x and y to put it in U ? I have tried some solutions as z[0 : 1 :...] but it doesnt work ? How can I do it ?
I bet
L_l(which is not defined) stands forL_lambda.