Hello,
I am trying to solve a Non-linear optimization problem. I have been using Knitro to solve non-linear problems, but this is my first time encountering this issue.
My lb range from 0 to 10,000 or -10,000 to 0 or -10,000 to 10,000. For an initial point, i use:
X=(lb+(ub-lb)).*rand(Nvar,1))'.
Here is how I call knitro:
options = knitro_options('maxit', 1000, 'outlev', 4, 'xtol', 1e-15, ...
'feastol', 1e-8, 'opttol', 1e-8, ...
'bar_maxcrossit', 5, 'honorbnds', 2, 'numthreads', 8);
[Xsqp, FUN, FLAG, ~,~,~,~] = knitro_nlp(@(X)SQP(X,Dat),X,[],[],Aeq,beq, ...
lb,ub,@(X)SQPnolcon(X,Dat),[],options,[]);
Here is the error I get:
WARNING: 1 constraint is constant or undefined and will be ignored.
=======================================
Trial License
(NOT FOR COMMERCIAL USE)
Artelys Knitro 14.0.0
=======================================
ERROR: Infeasible constraint deduced from presolve.
Deduced constraint value: Aeq(3026,:) = -1.34730000000000e-06
violates the constraint lower bound = 0.00000000000000e+00
Knitro presolver has deduced that constraint Aeq(3026,:) cannot be satisfied.
EXIT: Problem determined to be infeasible with respect to constraint bounds.
Final Statistics
----------------
Final objective value = 7.29751108473195e+03
Final feasibility error (abs / rel) = 8.46e+07 / 1.00e+00
Final optimality error (abs / rel) = 1.80e+308 / 1.80e+308
# of iterations = 0
# of CG iterations = 0
# of function evaluations = 10044
# of gradient evaluations = 0
Total program time (secs) = 0.39499 ( 1.827 CPU time)
Time spent in evaluations (secs) = 0.37890
Any help will be appreciated.