Why is this program Failed?

I already installed CVXQUAD. My code is here:

rmpath(‘D:\matlab\toolbox\signal\signal’);
%问题P1.5
cvx_begin
%优化变量, x1,y1为新的轨迹
variables t x1(101) y1(101)
%设置变量关系
maximize(t)
subject to
e1 = Puh0./a1;
e2 = Pu
h0./a2;
e3 = Pu*h0./a3;
c1 = P1.*h0./b1;
c2 = P2.*h0./b2;
c3 = P3.h0./b3;
%各用户可达速率的定义
R1ulb = a1.log(1+e1./(H^2+norms([x y]-repmat([s1x s1y],101,1),[],2).^2))./log(2)-a1/log(2).e1./((H^2+norms([x y]-repmat([s1x s1y],101,1),[],2).^2).(H^2+norms([x y]-repmat([s1x s1y],101,1),[],2).^2+e1)).(square_pos(norms([x1 y1]-repmat([s1x s1y],101,1),[],2))-norms([x y]-repmat([s1x s1y],101,1),[],2).^2);
R2ulb = a2.log(1+e2./(H^2+norms([x y]-repmat([s2x s2y],101,1),[],2).^2))./log(2)-a2/log(2).e2./((H^2+norms([x y]-repmat([s2x s2y],101,1),[],2).^2).(H^2+norms([x y]-repmat([s2x s2y],101,1),[],2).^2+e2)).(square_pos(norms([x1 y1]-repmat([s2x s2y],101,1),[],2))-norms([x y]-repmat([s2x s2y],101,1),[],2).^2);
R3ulb = a3.log(1+e3./(H^2+norms([x y]-repmat([s3x s3y],101,1),[],2).^2))./log(2)-a3/log(2).e3./((H^2+norms([x y]-repmat([s3x s3y],101,1),[],2).^2).(H^2+norms([x y]-repmat([s3x s3y],101,1),[],2).^2+e3)).(square_pos(norms([x1 y1]-repmat([s3x s3y],101,1),[],2))-norms([x y]-repmat([s3x s3y],101,1),[],2).^2);
R1vlb = b1.log(1+c1./(H^2+norms([x y]-repmat([d1x d1y],101,1),[],2).^2))./log(2)-b1/log(2).c1./((H^2+norms([x y]-repmat([d1x d1y],101,1),[],2).^2).(H^2+norms([x y]-repmat([d1x d1y],101,1),[],2).^2+c1)).(square_pos(norms([x1 y1]-repmat([d1x d1y],101,1),[],2))-norms([x y]-repmat([d1x d1y],101,1),[],2).^2);
R2vlb = b2.log(1+c2./(H^2+norms([x y]-repmat([d2x d2y],101,1),[],2).^2))./log(2)-b2/log(2).c2./((H^2+norms([x y]-repmat([d2x d2y],101,1),[],2).^2).(H^2+norms([x y]-repmat([d2x d2y],101,1),[],2).^2+c2)).(square_pos(norms([x1 y1]-repmat([d2x d2y],101,1),[],2))-norms([x y]-repmat([d2x d2y],101,1),[],2).^2);
R3vlb = b3.log(1+c3./(H^2+norms([x y]-repmat([d3x d3y],101,1),[],2).^2))./log(2)-b3/log(2).c3./((H^2+norms([x y]-repmat([d3x d3y],101,1),[],2).^2).(H^2+norms([x y]-repmat([d3x d3y],101,1),[],2).^2+c3)).(square_pos(norms([x1 y1]-repmat([d3x d3y],101,1),[],2))-norms([x y]-repmat([d3x d3y],101,1),[],2).^2);
%约束条件(22a)和(22b),因为有六个用户,所以分为六个式子0.
B0/(101
Rt)sum(R1ulb) >= t;
B0/(101
Rt)sum(R2ulb) >= t;
B0/(101
Rt)sum(R3ulb) >= t;
B0/(101
Rt)sum(R1vlb) >= t;
B0/(101
Rt)sum(R2vlb) >= t;
B0/(101
Rt)*sum(R3vlb) >= t;
%约束条件(18h)和(18i)
for i=1:100
norm([x1(i+1)-x1(i),y1(i+1)-y1(i)]) <= Dmax
end
x1(101) == x1(1);
y1(101) == y1(1);

cvx_end

Calling SDPT3 4.0: 5255 variables, 2530 equality constraints

num. of constraints = 2530
dim. of sdp var = 1212, num. of sdp blk = 606
dim. of socp var = 2118, num. of socp blk = 706
dim. of linear var = 1318
dim. of free var = 1 *** convert ublk to lblk


SDPT3: Infeasible path-following algorithms


version predcorr gam expon scale_data
HKM 1 0.000 1 0
it pstep dstep pinfeas dinfeas gap prim-obj dual-obj cputime

0|0.000|0.000|9.2e+01|5.7e+02|8.3e+09| 8.562165e-08 0.000000e+00| 0:0:00| spchol 1 1
1|0.175|0.278|7.6e+01|4.1e+02|9.8e+09| 3.313229e+04 1.329719e+07| 0:0:00| spchol 1 1
2|0.405|0.227|4.5e+01|3.2e+02|6.8e+09| 1.109833e+05 1.369032e+07| 0:0:00| spchol 1 1
3|0.737|0.144|1.2e+01|2.7e+02|2.6e+09| 1.821433e+05 1.459736e+07| 0:0:00| spchol 1 1
4|0.469|0.824|6.3e+00|4.8e+01|8.2e+08| 1.971537e+05 3.635564e+06| 0:0:00| spchol 1 1
5|0.873|0.518|8.1e-01|2.3e+01|2.3e+08| 2.039407e+05 6.220728e+06| 0:0:00| spchol 1 1
6|0.778|0.811|1.8e-01|4.3e+00|4.5e+07| 1.812271e+05 1.414946e+07| 0:0:00| spchol 1 1
7|0.584|0.408|7.4e-02|2.6e+00|2.6e+07| 1.235438e+05 1.892166e+07| 0:0:00| spchol 1 1
8|0.571|0.226|3.2e-02|2.0e+00|2.0e+07| 7.721465e+04 2.316141e+07| 0:0:00| spchol 1 1
9|0.494|0.168|1.6e-02|1.7e+00|2.0e+07| 4.983172e+04 2.765436e+07| 0:0:00| spchol 1 1
10|0.329|0.267|1.1e-02|1.2e+00|1.6e+07| 3.786508e+04 2.942860e+07| 0:0:00| spchol 1 1
11|0.421|0.154|6.3e-03|1.0e+00|1.8e+07| 2.762281e+04 2.994177e+07| 0:0:00| spchol 1 1
12|0.622|0.682|2.4e-03|3.3e-01|7.1e+06| 1.605035e+04 1.114075e+07| 0:0:00| spchol 1 1
13|1.000|0.717|5.5e-10|9.3e-02|2.5e+06| 3.042316e+03 3.082868e+06| 0:0:01| spchol 1 1
14|0.985|0.982|1.0e-09|1.7e-03|4.7e+04| 5.098957e+01 6.099230e+04| 0:0:01| spchol 1 1
15|0.938|0.972|8.2e-11|7.3e-05|2.0e+03| 3.704109e+00 5.131977e+03| 0:0:01| spchol 1 1
16|1.000|0.994|2.7e-10|1.6e-05|4.3e+02| 3.377098e+00 1.623700e+03| 0:0:01| spchol 1 1
17|0.985|0.874|1.5e-11|8.6e-06|8.3e+01| 1.990140e+00 9.641385e+02| 0:0:01| spchol 1 1
18|1.000|0.515|1.8e-12|6.1e-06|4.4e+01| 1.607650e+00 6.893660e+02| 0:0:01| spchol 1 1
19|1.000|0.400|4.9e-12|4.5e-06|2.9e+01| 1.402665e+00 5.000547e+02| 0:0:01| spchol 1 1
20|0.874|0.372|1.4e-11|2.9e-06|2.2e+01| 1.154380e+00 3.216678e+02| 0:0:01| spchol 1 1
21|0.941|0.251|2.8e-12|2.2e-06|2.0e+01| 9.199794e-01 2.410668e+02| 0:0:01| spchol 1 1
22|1.000|0.318|9.6e-12|1.5e-06|1.7e+01| 7.143506e-01 1.641553e+02| 0:0:01| spchol 1 1
23|1.000|0.305|2.3e-11|1.0e-06|1.4e+01| 5.235801e-01 1.138759e+02| 0:0:01| spchol 1 1
24|1.000|0.294|4.8e-12|7.2e-07|1.2e+01| 3.693929e-01 8.024946e+01| 0:0:01| spchol 1 1
25|1.000|0.307|8.5e-11|5.0e-07|9.6e+00| 2.417462e-01 5.546235e+01| 0:0:01| spchol 1 2
26|0.799|0.305|9.5e-11|3.5e-07|7.7e+00| 1.641930e-01 3.843302e+01| 0:0:01| spchol 2 3
27|0.497|0.267|1.4e-08|2.5e-07|6.2e+00| 2.245242e-01 2.803041e+01| 0:0:01| spchol 2 2
28|0.918|0.186|1.2e-09|2.1e-07|5.8e+00| 1.291123e-01 2.275062e+01| 0:0:01| spchol 3 3
29|1.000|0.252|2.8e-09|1.6e-07|5.1e+00| 9.828904e-02 1.692232e+01| 0:0:01| spchol 3 3
30|1.000|0.295|1.1e-08|1.1e-07|4.2e+00| 7.741263e-02 1.183293e+01| 0:0:01| spchol 4 3
31|1.000|0.317|6.2e-09|7.5e-08|3.3e+00| 5.741044e-02 8.007705e+00| 0:0:01| spchol 4 3
32|1.000|0.306|2.1e-08|5.2e-08|2.7e+00| 4.240469e-02 5.500165e+00| 0:0:01| spchol 4 5
33|1.000|0.305|4.7e-08|3.6e-08|2.1e+00| 3.006942e-02 3.777712e+00| 0:0:01| spchol 4 5
34|0.726|0.299|1.3e-08|2.5e-08|1.7e+00| 1.992383e-02 2.611839e+00| 0:0:01| spchol 8 12
35|0.267|0.069|6.0e-07|2.4e-08|1.6e+00| 5.332677e-02 2.421343e+00| 0:0:01| spchol 4 4
36|0.638|0.160|2.1e-07|2.0e-08|1.5e+00| 2.998834e-02 2.017743e+00| 0:0:01| spchol 13 30
37|0.531|0.158|8.5e-07|1.7e-08|1.4e+00| 5.053627e-02 1.679930e+00| 0:0:01| spchol 13 15
38|1.000|0.247|8.2e-08|1.3e-08|1.2e+00| 1.811189e-02 1.243834e+00| 0:0:01| spchol 28 30
39|1.000|0.186|4.7e-02|1.1e-08|1.3e+00|-1.772628e+03 9.850157e-01| 0:0:01|
stop: progress is too slow
stop: progress is bad*

number of iterations = 39
primal objective value = -1.77262836e+03
dual objective value = 9.85015670e-01
gap := trace(XZ) = 1.27e+00
relative gap = 7.15e-04
actual relative gap = -9.99e-01
rel. primal infeas (scaled problem) = 4.73e-02
rel. dual " " " = 1.08e-08
rel. primal infeas (unscaled problem) = 0.00e+00
rel. dual " " " = 0.00e+00
norm(X), norm(y), norm(Z) = 2.2e+08, 6.4e-01, 6.4e-01
norm(A), norm(b), norm© = 7.6e+01, 9.4e+04, 2.4e+00
Total CPU time (secs) = 1.24
CPU time per iteration = 0.03
termination code = -5
DIMACS: 9.5e-01 0.0e+00 1.3e-08 0.0e+00 -1.0e+00 7.1e-04


Status: Failed
Optimal value (cvx_optval): NaN

Try another solver, such as Mosek, if available.

Check and try to improve the scaling of the data in the problem.