[pdp.aero]

Hi Guys,


Just a quick question. Have been trying to somehow accelerate the AD part of an optimization problem because it takes lots of iteration to converge. I mean the primal converge in less than 20k but for AD it goes to somewhat 300k iterations. So I have been trying to modify the below part of my config file to make it converge faster then I got this error:



Error in "void CSymmetricMatrix::CholeskyDecompose()":
-------------------------------------------------------------------------
LLT factorization failed.
------------------------------ Error Exit -------------------------------


Could you please explain what could be the reason?



% ---------------- ADJOINT-FLOW NUMERICAL METHOD DEFINITION -------------------%
%
% Frozen the slope limiter in the discrete adjoint formulation (NO, YES)
FROZEN_LIMITER_DISC= NO
%
% Frozen the turbulent viscosity in the discrete adjoint formulation (NO, YES)
FROZEN_VISC_DISC= NO
%
% Use an inconsistent spatial integration (primal-dual) in the discrete
% adjoint formulation. The AD will use the numerical methods in
% the ADJOINT-FLOW NUMERICAL METHOD DEFINITION section (NO, YES)
INCONSISTENT_DISC= NO
%
% Convective numerical method (JST, LAX-FRIEDRICH, ROE)
CONV_NUM_METHOD_ADJFLOW= JST
%
% Time discretization (RUNGE-KUTTA_EXPLICIT, EULER_IMPLICIT)
TIME_DISCRE_ADJFLOW= EULER_IMPLICIT
%
% Relaxation coefficient (also for discrete adjoint problems)
RELAXATION_FACTOR_ADJOINT= 0.8
%
% Enable (if != 0) quasi-Newton acceleration/stabilization of discrete adjoints
QUASI_NEWTON_NUM_SAMPLES= 20
%
% Reduction factor of the CFL coefficient in the adjoint problem
CFL_REDUCTION_ADJFLOW= 0.8
%
% Limit value for the adjoint variable
LIMIT_ADJFLOW= 1E6
%
% Use multigrid in the adjoint problem (NO, YES)
MG_ADJFLOW= NO



Is it relaxation factor or number of sampling for quasi-Newton acceleration?