I've tried all sorts of initial conditions but this just won't converge. It is an easy Lagrange multipliers problem.
var("y z L")
F(x, y, z, L) = (x-3)^2 + (y-1)^2 + (z + 1)^2 + L*(x^2 + y^2 + z^2 - 4)
minimize(F, [4, 3, 2, 1], algorithm='ncg' )Any thoughts? Are there ways of forcing it to use more iterations, or something??
I've done a bit of digging. You can set the max number of iterations and the tolerance.
minimize(F, [4,3,2,1], algorithm='ncg', avextol=10^(-30) , maxiter =10000 )These get passed to scipy. However, this does not resolve your issue.
I agree with your symbolic approach! Here a different way to code it:
soln=solve(list(F.gradient()),[x,y,z,L])%% step one: var("y z L")
F(x, y, z, L) = (x-3)^2 + (y-1)^2 + (z+1)^2 + L*(x^2 + y^2 + z^2 - 4)
derivative(F)
%% step two:
eqn1= 2Lx + 2*x - 6 == 0
eqn2= 2Ly + 2*y - 2 == 0
eqn3= 2Lz + 2*z + 2 == 0
eqn4= x^2 + y^2 + z^2 - 4 == 0
solve( [eqn1, eqn2, eqn3, eqn4], [x, y, z, L] )
%% correct answer obtained: [[x == -6/11sqrt(11), y == -2/11sqrt(11), z == 2/11sqrt(11), L == -1/2sqrt(11) - 1],
[x == 6/11sqrt(11), y == 2/11sqrt(11), z == -2/11sqrt(11), L == 1/2sqrt(11) - 1]]
%% moral of the story: if symbolic methods work fine, why bother with numerical methods?! ;-)
Asked: 10 years ago
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Last updated: Jul 04 '14
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It appears to have worked for me in SMC. Here is the output: Optimization terminated successfully. Current function value: 2.032982 Iterations: 3 Function evaluations: 57 Gradient evaluations: 3 Hessian evaluations: 3 (1.48641364243, 0.860982992316, 0.536302359521, 0.251059261969)
When I input the second line I get SyntaxError. With plus signs inserted I also get the succesful result. This is Sage-6.3beta5. SMC is Sage-6.2. Which version is your question about?
rws, I'm not sure where the plus signs went. They definitely belong. It must have been an error during cut-and-paste. I was using the Sage Single-Cell Server.
calc314, I'm sorry, but that's not correct. See where it says "Current function value: 2.032982"? That should be zero. Also, it does not match the symbolic solution at all. Perhaps this is a bug, and minimize should give an error message, since the minimum wasn't reached?