What is causing the final Status: Inaccurate/Solved? and how can I modify my code to get it Solved?


I can’t figure out why the result I get has status inaccurate/solved.

I have the following optimization problem:

I have formulated the above optimization problem as follows:

K= 2;
N= 2;
NumberOfUsersInSubcarrier (1:N)=0;
NOMA_max = 2;
UE_SCid (1:K) = 0;
R_max = 140;
L = 15 * 10^4;
B_sub = B/N;
z = 10^-28; % effective capacitance coefficient
C = 10^2; % number of CPU cycles required per bit

for i=1:K
CG (i) = complex(randn(1),randn(1))/sqrt(2); % exponential random distribution with mean value 1

for i=1:K
SC_id = randi(N);
while (NumberOfUsersInSubcarrier(SC_id)>=NOMA_max) % each SBS can serve up to NOMA_max_users*N users
SC_id = randi(N);
NumberOfUsersInSubcarrier(SC_id)=NumberOfUsersInSubcarrier(SC_id) + 1;
UE_SCid (i) = SC_id; % save the UE’s subcarrier

variable p(2)
variable l(2)
variable r(2)

minimize sum(z*C^3*pow_p(l,3)/T^2+p*T)

subject to
    r >= (L-l)./(B_sub*T)   % Rate constraint
    p >= 0                  % Power constraint
    l >= 0                  % Local data constraint 1
    l <= L                  % Local data constraint 2

    % MAC contraints
    r(1) <= log(1+p(1)*norm(CG(1))^2)
    r(2) <= log(1+p(2)*norm(CG(2))^2)
    r(1)+r(2) <= log(1+p(1)*norm(CG(1))^2 + p(2)*norm(CG(2))^2)


After running the problem I get the following message result:

>> cvx_code
CVX Warning:
_ Models involving “log” or other functions in the log, exp, and entropy_
_ family are solved using an experimental successive approximation method._
_ This method is slower and less reliable than the method CVX employs for_
_ other models. Please see the section of the user’s guide entitled_
_ The successive approximation method_
_ for more details about the approach, and for instructions on how to_
_ suppress this warning message in the future._
_ _
Successive approximation method to be employed.
_ For improved efficiency, SDPT3 is solving the dual problem._
_ SDPT3 will be called several times to refine the solution._
_ Original size: 29 variables, 10 equality constraints_
_ 3 exponentials add 24 variables, 15 equality constraints_
_ Cones | Errors |_
Mov/Act | Centering Exp cone Poly cone | Status
_ 2/ 2 | 7.550e+00 4.276e+00 0.000e+00 | Inaccurate/Solved_
_ 2/ 2 | 2.650e+00 4.322e-01 0.000e+00 | Solved_
_ 2/ 2 | 9.921e-02 6.676e-04 0.000e+00 | Solved_
_ 2/ 2 | 1.059e-02 7.579e-06 0.000e+00 | Solved_
_ 0/ 0 | 1.288e-03 0.000e+00 0.000e+00 | Inaccurate/Solved_
_ 0/ 0 | 1.268e-04 0.000e+00 0.000e+00 | Inaccurate/Solved_
_ 0/ 0 | 1.752e-04 0.000e+00 0.000e+00 | Inaccurate/Solved_
Status: Inaccurate/Solved
Optimal value (cvx_optval): +67.8698

Can someone please help me with what might be the cause of the Inaccurate/Solved status and what should I do in order to reach a solved status?

One of the conditions is ignored, thus resulting to inappropriate values
(Mark L. Stone) #2

I think Inaccurate/solved is resulting because of the poor scaling n the problem. In particular ,z = 1e-28 is causing difficulty in the objective function, because it makes the first additive term in the objective function of vastly different order of magnitude than the second term. As a result, the variable l is not well determined numerically to the solver. So as is, the optimal objective value is determined fairly well, but l is not well determined at all.

If z is chosen substantially larger, then the problem becomes better behaved.

Given your use of log, I advise you to follow the advice in CVXQUAD: How to use CVXQUAD's Pade Approximant instead of CVX's unreliable Successive Approximation for GP mode, log, exp, entr, rel_entr, kl_div, log_det, det_rootn, exponential cone. CVXQUAD's Quantum (Matrix) Entropy & Matrix Log related functions . In particular, change the MAC constraints to

r(1) <= -rel_entr(1,1+p(1)*norm(CG(1))^2)
r(2) <= -rel_entr(1,1+p(2)*norm(CG(2))^2)
r(1)+r(2) <= -rel_entr(1,1+p(1)*norm(CG(1))^2 + p(2)*norm(CG(2))^2)

However, this does not fix the Inaccurate/solved issue. You need to change z to do that.


You were absolutely right. I have made the changes to the code you recommended and I have also changed the value of z to 10^-16 based on your suggestions and the final status now is always ‘Solved’. Thanks a million!

However I am having another problem which I think is relevant to this topic, therefore I will ask here instead of opening another thread.

The final values of p after the cvx has been solved, are always greater or equal to zero (which has been set as one of the constraint) and the maximum value I have gotten after running my code 1 million times was more or less around 2000. However, after adding another constraint for the maximum value of p:

p <= P_max

where P_max = (zC^3L^3)/(T^2) = 33750000, I thought this constraint could not possibly cause any problems since the value of p always ends up to be significantly lower than that. But instead, now the final status has become:

Status: Failed
Optimal value (cvx_optval): NaN

and the final p values of the failed convex game end up being:

p =

1.0e+07 *


I can’t understand how this constraint is causing the cvx problem to fail, especially since the P_max has been set so high which should not cause any changes at all based on the greatest value of p when no maximum limit constraint has been set. Can someone share some wisdom pleeease?

(Mark L. Stone) #4

Large input numbers anywhere in your problem specification can cause problems for the solvers called by CVX. it is true that the theoretical so0lution is unaffected by adding a non-binding constraint to what was the optimal solution, But doing so can cause havoc with solvers. Either use a relatively small magnitude bound constraint, or don;t use a bound constraint at all.