What can I do to improve my problem


Successive approximation method to be employed.
SDPT3 will be called several times to refine the solution.
Original size: 398 variables, 170 equality constraints
15 exponentials add 120 variables, 75 equality constraints

Cones | Errors |
Mov/Act | Centering Exp cone Poly cone | Status
--------±--------------------------------±--------
11/ 11 | 1.481e+00 1.327e-01 0.000e+00 | Solved
3/ 3 | 2.680e-01 4.370e-03 0.000e+00 | Solved
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Solved

Status: Solved
Optimal value (cvx_optval): +143.367

Successive approximation method to be employed.
SDPT3 will be called several times to refine the solution.
Original size: 398 variables, 170 equality constraints
15 exponentials add 120 variables, 75 equality constraints

Cones | Errors |
Mov/Act | Centering Exp cone Poly cone | Status
--------±--------------------------------±--------
11/ 11 | 8.145e-01 4.075e-02 0.000e+00 | Solved
4/ 4 | 1.166e-01 8.351e-04 0.000e+00 | Solved
1/ 1 | 1.790e-02 4.281e-06 0.000e+00 | Solved
0/ 0 | 1.743e-03 0.000e+00 0.000e+00 | Solved

Status: Solved
Optimal value (cvx_optval): +105.734

Successive approximation method to be employed.
SDPT3 will be called several times to refine the solution.
Original size: 398 variables, 170 equality constraints
15 exponentials add 120 variables, 75 equality constraints

Cones | Errors |
Mov/Act | Centering Exp cone Poly cone | Status
--------±--------------------------------±--------
12/ 12 | 8.580e-01 4.517e-02 0.000e+00 | Solved
6/ 6 | 6.346e-02 2.351e-04 0.000e+00 | Solved
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Solved

Status: Solved
Optimal value (cvx_optval): +68.1837

Successive approximation method to be employed.
SDPT3 will be called several times to refine the solution.
Original size: 398 variables, 170 equality constraints
15 exponentials add 120 variables, 75 equality constraints

Cones | Errors |
Mov/Act | Centering Exp cone Poly cone | Status
--------±--------------------------------±--------
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Failed
1/ 1 | 8.000e+00 2.216e+00 0.000e+00 | Failed
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Failed
1/ 1 | 8.000e+00 2.171e+00 0.000e+00 | Failed
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Failed

Status: Failed
Optimal value (cvx_optval): NaN

Successive approximation method to be employed.
SDPT3 will be called several times to refine the solution.
Original size: 398 variables, 170 equality constraints
15 exponentials add 120 variables, 75 equality constraints

Cones | Errors |
Mov/Act | Centering Exp cone Poly cone | Status
--------±--------------------------------±--------
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Failed
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Failed
0/ 0 | 0.000e+00 0.000e+00 0.000e+00 | Failed

Status: Failed
Optimal value (cvx_optval): NaN

What are the possible ways to solve this kind of situation where the first few times are solved and the latter is failed

If you have access to Mosek 9.x, use CVX 2.2, with Mosek as solver.

Otherwise, follow the advice at 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

However, if you are solving a sequence of CVX problems as part of Successive Convex Approximation (SCA), it may that your overall SCA algorithm is unstable and diverges, thereby creating more difficult to solve CVX problems. If you follow the advice in the link, you may or may not succeed in soving all the CVX problems You might be better off using a non-convex solver available under YALMIP than using SCA if your name doesn’t happen to be Stephen Boyd, which it’s not.