If your function is too complicated to evaluate derivatives, chances are that it's not convex. But you're still going to need the first and second order derivatives for Newton's method... If you want to solve min. f(x) s.t. A*x = b you could first find a feasible point x0 satisfying A*x0 = b (e.g., the least-norm solution to A*x = b) and parametrize all feasible points as z = x0+ B*y where B spans the nullspace of A, i.e., A*B = 0. Now you have an unconstrained problem min. f( x0 + B*y ) over the new variable y. On 6/15/07, fdu.xiaojf@gmail.com <fdu.xiaojf@gmail.com> wrote:
What kind of function are you minimizing?
CVXOPT handles convex functions with convex inequality constraints and linear equality constraints.
If your function is non-convex, couldn't you eliminate your linear equality constraints and try Newton's method for the unconstrained
Joachim Dahl wrote: problem?
Can you give me some hints on how to eliminate the linear equality constraints ?
How to judge if a function is non-convex? The expression of my function is too complex to calculate the derivative.
Thanks.
Xiao Jianfeng
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