NaN appears in iteration

Hello everyone, I really need your help. Why does NaN appear in my results? I really want to know. Thank you all. here is my code:
clc;
clear all;
close all;
cvx_solver mosek
%轨迹初始化
T = 120; %mission period
delta = T/120; %elemental length
N = T/delta; %number of time slot
vmax=40;
K=6;

w1 = [700ones(N,1),100ones(N,1)];
w2 = [200ones(N,1),450ones(N,1)];
w3 = [-300ones(N,1),450ones(N,1)];
w4 = [-600ones(N,1),200ones(N,1)];
w5 = [-450ones(N,1),-450ones(N,1)];
w6 = [400ones(N,1),-500ones(N,1)];

% 恶意干扰在移动
% 干扰点的初始轨迹
Jx1 =0 ;%圆心
Jy1 = 0;
Jw1 = 200;%radius
theta = 0:2pi/(N-1):2pi;
J11 = Jx1 + Jw1cos(theta);
J21 = Jy1 + Jw1
sin(theta);
J11 = J11’;
J21 = J21’;
J = [J11,J21];
% J = [500ones(N,1),800ones(N,1)];%恶意干扰信号位置
pm=10;%1干扰功率
p1 = 1ones(N,1);%为什么当功率初始值为0.01时优化不可行
p2 = 1
ones(N,1);
p3 = 1ones(N,1);
p4 = 1
ones(N,1);
p5 = 1ones(N,1);
p6 = 1
ones(N,1);
pmax=0.1;
B=10;
vmax = 40; %maximum speed
beta_db = -60; %单位距离下信道增益 dB
sigma_db = -148; %噪声强度dB
beta = 10^(beta_db/10);
sigma = 10^(sigma_db/10);
H = 100; %无人机高度
%初始轨迹 初始化不同,轨迹也不同
x11 = 0;%圆心
y11 = 0;
r1 = 200;%radius
theta = 0:2pi/(N-1):2pi;
%初始化无人机轨迹
q11 = x11 - r1cos(theta);
q21 = y11 - r1
sin(theta);
% q11 = q11’;
% q21 = q21’;
% q1 = [q11,q21];
q1=[38ones(N,1),5ones(N,1)];
q1s=q1;
%无人机与用户位置d_r,u
for cntTra=1:1:3
d11 = H^2+(q1s(:,1)-w1(:,1)).^2+(q1s(:,2)-w1(:,2)).^2;
d12 = H^2+(q1s(:,1)-w2(:,1)).^2+(q1s(:,2)-w2(:,2)).^2;
d13 = H^2+(q1s(:,1)-w3(:,1)).^2+(q1s(:,2)-w3(:,2)).^2;
d14 = H^2+(q1s(:,1)-w4(:,1)).^2+(q1s(:,2)-w4(:,2)).^2;
d15 = H^2+(q1s(:,1)-w5(:,1)).^2+(q1s(:,2)-w5(:,2)).^2;
d16 = H^2+(q1s(:,1)-w6(:,1)).^2+(q1s(:,2)-w6(:,2)).^2;
d= H^2+(q1s(:,1)-J(:,1)).^2+(q1s(:,2)-J(:,2)).^2;

Is=sigma+(beta.*pm)./d;%初始可行解
Ls1=d11./(beta.*p1);
Ls2=d12./(beta.*p2);
Ls3=d13./(beta.*p3);
Ls4=d14./(beta.*p4);
Ls5=d15./(beta.*p5);
Ls6=d16./(beta.*p6);
LIs1=Ls1.*Is;
LIs2=Ls2.*Is;
LIs3=Ls3.*Is;
LIs4=Ls4.*Is;
LIs5=Ls5.*Is;
LIs6=Ls6.*Is;

A1=-B./(Ls1+Ls1.^2.*Is);%泰勒展开系数
A2=-B./(Ls2+Ls2.^2.*Is);
A3=-B./(Ls3+Ls3.^2.*Is);
A4=-B./(Ls4+Ls4.^2.*Is);
A5=-B./(Ls5+Ls5.^2.*Is);
A6=-B./(Ls6+Ls6.^2.*Is);

C1=-B./(Is+Is.^2.*Ls1);%泰勒展开系数
C2=-B./(Is+Is.^2.*Ls2);
C3=-B./(Is+Is.^2.*Ls3);
C4=-B./(Is+Is.^2.*Ls4);
C5=-B./(Is+Is.^2.*Ls5);
C6=-B./(Is+Is.^2.*Ls6);

cvx_begin

variable q1(N,2)
variable L1(N)
variable L2(N)
variable L3(N)
variable L4(N)
variable L5(N)
variable L6(N)
variable I(N)

variable eta
variable m(N)
expression qu1(N,1)

maximize eta

subject to

% B.log(1+1./(LIs1))+A1.(L1(:)-Ls1(:))+C1.(I(:)-Is(:))>=eta;%(1-1)
% B.log(1+1./(LIs2))+A2.(L2(:)-Ls2(:))+C2.
(I(:)-Is(:))>=eta;
% B.log(1+1./(LIs3))+A3.(L3(:)-Ls3(:))+C3.(I(:)-Is(:))>=eta;
% B.log(1+1./(LIs4))+A4.(L4(:)-Ls4(:))+C4.
(I(:)-Is(:))>=eta;
% B.log(1+1./(LIs5))+A5.(L5(:)-Ls5(:))+C5.(I(:)-Is(:))>=eta;
% B.log(1+1./(LIs6))+A6.(L6(:)-Ls6(:))+C6.
(I(:)-Is(:))>=eta;

sum(B.log(1+1./(LIs1))+A1.(L1(:)-Ls1(:))+C1.(I(:)-Is(:)))/T>=eta;%(1-1)
sum(B.log(1+1./(LIs2))+A2.(L2(:)-Ls2(:))+C2.
(I(:)-Is(:)))/T>=eta;
sum(B.log(1+1./(LIs3))+A3.(L3(:)-Ls3(:))+C3.(I(:)-Is(:)))/T>=eta;
sum(B.log(1+1./(LIs4))+A4.(L4(:)-Ls4(:))+C4.
(I(:)-Is(:)))/T>=eta;
sum(B.log(1+1./(LIs5))+A5.(L5(:)-Ls5(:))+C5.(I(:)-Is(:)))/T>=eta;
sum(B.log(1+1./(LIs6))+A6.(L6(:)-Ls6(:))+C6.
(I(:)-Is(:)))/T>=eta;

for i=1:1:N

H^2+sum_square_abs(q1(i,:)-w1(i,:))<=beta.*p1.*L1(i)%(1-2)
H^2+sum_square_abs(q1(i,:)-w2(i,:))<=beta.*p2.*L2(i);
H^2+sum_square_abs(q1(i,:)-w3(i,:))<=beta.*p3.*L3(i);
H^2+sum_square_abs(q1(i,:)-w4(i,:))<=beta.*p4.*L4(i);
H^2+sum_square_abs(q1(i,:)-w5(i,:))<=beta.*p5.*L5(i);
H^2+sum_square_abs(q1(i,:)-w6(i,:))<=beta.*p6.*L6(i);

pm*beta.*inv_pos(m(i))+sigma<=I(i)%(1-3)

end

for i=1:1:N
qu1(i)=2*q1s(i,1)q1(i,1)-q1s(i,1)^2+J(i,1)^2-2J(i,1)q1(i,1)+2q1s(i,2)q1(i,2)-q1s(i,2)^2+J(i,2)^2-2J(i,2)*q1(i,2)+H^2;%一阶泰勒
end

m<=qu1;%1-4
m>=0;%(1-5)
q1(1,:slight_smile: == [38,5];
q1(N,:slight_smile: == [38,5];
for i = 1:1:N-1
norm(q1(i+1,:)-q1(i,:))<=vmax*delta;
end

cvx_end
q1s=q1;
optvalue1(cntTra) = cvx_optval;
end

plot(q1(:,1),q1(:,2),‘r-’);

and the cvx result,

MOSEK Version 9.1.9 (Build date: 2019-11-21 11:34:40)
Copyright © MOSEK ApS, Denmark. WWW: mosek.com
Platform: Windows/64-X86

MOSEK warning 710: #119 (nearly) zero elements are specified in sparse col ‘’ (2) of matrix ‘A’.
MOSEK warning 710: #119 (nearly) zero elements are specified in sparse col ‘’ (3) of matrix ‘A’.
MOSEK warning 710: #119 (nearly) zero elements are specified in sparse col ‘’ (4) of matrix ‘A’.
MOSEK warning 710: #119 (nearly) zero elements are specified in sparse col ‘’ (5) of matrix ‘A’.
MOSEK warning 710: #119 (nearly) zero elements are specified in sparse col ‘’ (6) of matrix ‘A’.
MOSEK warning 710: #119 (nearly) zero elements are specified in sparse col ‘’ (7) of matrix ‘A’.
Problem
Name :
Objective sense : min
Type : CONIC (conic optimization problem)
Constraints : 2158
Cones : 959
Scalar variables : 4804
Matrix variables : 0
Integer variables : 0

Optimizer started.
Presolve started.
Linear dependency checker started.
Linear dependency checker terminated.
Eliminator started.
Freed constraints in eliminator : 119
Eliminator terminated.
Eliminator started.
Freed constraints in eliminator : 0
Eliminator terminated.
Eliminator - tries : 2 time : 0.00
Lin. dep. - tries : 1 time : 0.01
Lin. dep. - number : 0
Presolve terminated. Time: 0.03
Problem
Name :
Objective sense : min
Type : CONIC (conic optimization problem)
Constraints : 2158
Cones : 959
Scalar variables : 4804
Matrix variables : 0
Integer variables : 0

Optimizer - threads : 8
Optimizer - solved problem : the primal
Optimizer - Constraints : 1911
Optimizer - Cones : 959
Optimizer - Scalar variables : 4545 conic : 3585
Optimizer - Semi-definite variables: 0 scalarized : 0
Factor - setup time : 0.00 dense det. time : 0.00
Factor - ML order time : 0.00 GP order time : 0.00
Factor - nonzeros before factor : 1.46e+04 after factor : 1.63e+04
Factor - dense dim. : 0 flops : 9.18e+05
ITE PFEAS DFEAS GFEAS PRSTATUS POBJ DOBJ MU TIME
0 1.0e+00 1.2e+06 5.6e+06 0.00e+00 -5.634814049e+06 0.000000000e+00 1.0e+00 0.05
1 1.5e-01 1.8e+05 2.2e+06 -1.00e+00 -5.649644305e+06 -1.568614313e+04 1.5e-01 0.13
2 2.2e-02 2.6e+04 8.3e+05 -1.00e+00 -5.678186866e+06 -4.927101573e+04 2.2e-02 0.13
3 2.6e-03 3.1e+03 2.9e+05 -9.98e-01 -5.695087022e+06 -9.595615278e+04 2.6e-03 0.13
4 5.9e-04 7.1e+02 1.4e+05 -9.90e-01 -5.623862993e+06 -9.930221110e+04 5.9e-04 0.14
5 2.7e-04 3.2e+02 9.0e+04 -9.72e-01 -5.763864821e+06 -3.066820719e+05 2.7e-04 0.14
6 7.7e-05 9.1e+01 4.6e+04 -9.55e-01 -6.232046289e+06 -1.008782171e+06 7.7e-05 0.16
7 2.4e-05 2.9e+01 2.4e+04 -8.97e-01 -8.380284687e+06 -3.568447592e+06 2.4e-05 0.16
8 5.0e-06 5.9e+00 8.5e+03 -7.82e-01 -1.332767528e+07 -9.747378562e+06 5.0e-06 0.16
9 1.4e-06 1.7e+00 2.5e+03 -3.42e-01 -8.284920382e+06 -6.287267148e+06 1.4e-06 0.16
10 1.2e-06 1.4e+00 2.0e+03 3.72e-01 -6.751654569e+06 -5.004240253e+06 1.2e-06 0.16
11 3.7e-07 4.4e-01 3.4e+02 4.98e-01 -2.716411372e+06 -2.101315368e+06 3.7e-07 0.17
12 3.6e-08 4.3e-02 9.7e+00 8.97e-01 -2.700101022e+05 -2.092838589e+05 3.6e-08 0.17
13 5.7e-11 6.8e-05 5.8e-04 9.96e-01 -4.847685805e+02 -3.887267339e+02 5.7e-11 0.17
14 4.0e-12 4.7e-06 1.1e-05 9.91e-01 -4.539855750e+01 -3.861045693e+01 4.0e-12 0.17
15 2.7e-12 1.2e-06 1.5e-06 9.08e-01 -1.592128186e+01 -1.410469848e+01 9.9e-13 0.19
16 2.6e-12 1.2e-06 1.5e-06 8.04e-01 -1.585841787e+01 -1.404821190e+01 9.8e-13 0.19
17 2.6e-12 1.2e-06 1.5e-06 8.04e-01 -1.585055398e+01 -1.404115558e+01 9.8e-13 0.20
18 1.2e-12 5.2e-07 5.0e-07 8.04e-01 -6.872241777e+00 -5.978292074e+00 4.3e-13 0.20
19 4.6e-13 2.0e-07 1.4e-07 8.32e-01 -3.255482987e+00 -2.865309861e+00 1.7e-13 0.20
20 2.2e-13 9.9e-08 5.5e-08 7.40e-01 -6.112237008e-01 -3.917522776e-01 8.3e-14 0.22
21 2.2e-13 9.9e-08 5.5e-08 8.69e-01 -6.112237008e-01 -3.917522776e-01 8.3e-14 0.22
22 2.2e-13 9.9e-08 5.5e-08 9.22e-01 -6.110700817e-01 -3.916564226e-01 8.3e-14 0.23
23 2.2e-13 9.9e-08 5.5e-08 9.30e-01 -6.106432433e-01 -3.913322366e-01 8.3e-14 0.23
24 2.2e-13 9.9e-08 5.5e-08 9.30e-01 -6.106432433e-01 -3.913322366e-01 8.3e-14 0.23
25 2.1e-13 9.1e-08 4.8e-08 9.77e-01 -5.448428642e-01 -3.425804225e-01 7.7e-14 0.25
26 2.1e-13 9.0e-08 4.8e-08 9.81e-01 -5.370402990e-01 -3.367845338e-01 7.6e-14 0.25
27 2.1e-13 9.0e-08 4.7e-08 9.81e-01 -5.331878888e-01 -3.339127867e-01 7.5e-14 0.25
28 2.1e-13 9.0e-08 4.7e-08 9.81e-01 -5.331878888e-01 -3.339127867e-01 7.5e-14 0.25
29 2.1e-13 9.0e-08 4.7e-08 9.89e-01 -5.331878888e-01 -3.339127867e-01 7.5e-14 0.27
Optimizer terminated. Time: 0.31

Interior-point solution summary
Problem status : UNKNOWN
Solution status : UNKNOWN
Primal. obj: -5.3318788877e-01 nrm: 4e+09 Viol. con: 6e-03 var: 3e-08 cones: 0e+00
Dual. obj: -3.3391278675e-01 nrm: 4e+12 Viol. con: 0e+00 var: 3e-02 cones: 0e+00
Optimizer summary
Optimizer - time: 0.31
Interior-point - iterations : 30 time: 0.28
Basis identification - time: 0.00
Primal - iterations : 0 time: 0.00
Dual - iterations : 0 time: 0.00
Clean primal - iterations : 0 time: 0.00
Clean dual - iterations : 0 time: 0.00
Simplex - time: 0.00
Primal simplex - iterations : 0 time: 0.00
Dual simplex - iterations : 0 time: 0.00
Mixed integer - relaxations: 0 time: 0.00


Status: Failed
Optimal value (cvx_optval): NaN

Why does NaN appear in my second iteration? thank you.

Mosek warned about near zero elements in the input data, and wasn’t able to reliably conclude it found the optimum. In such cases, CVX considers the solver to have failed and returns NaN for CVX variablea and optimal objective value.

Either you have bad numerical scaling to begin with, or the output of CVX problems becomes wilder and wilder, which leads to numerically terrible optimization problems. Read my posts on this forum pertaining to the perils of (unsafeguarded) Successive Convex Approximation (SCA).

This seems similar to your previous question. Hello,how to deal with it?Status: Infeasible Optimal value (cvx_optval): -Inf

thank you Mark, I tried to scale the data, but a new problem came. How can I solve it?
clc;
clear all;
close all;
cvx_solver mosek
%轨迹初始化
T = 120; %mission period
delta = T/120; %elemental length
% N = T/delta; %number of time slot
N=60;
K=6;

% 用户在移动
% 用户一的初始轨迹
% xw1 = 700;%圆心
% yw1 = 100;
% rw1 = 200;%radius
% theta = 0:2pi/(N-1):2pi;
% qw11 = xw1 + rw1cos(theta);
% qw21 = yw1 + rw1
sin(theta);
% qw11 = qw11’;
% qw21 = qw21’;
% w1 = [qw11,qw21];
w1 = [700ones(N,1),100ones(N,1)];
w2 = [200ones(N,1),450ones(N,1)];
w3 = [-300ones(N,1),450ones(N,1)];
w4 = [-600ones(N,1),200ones(N,1)];
w5 = [-450ones(N,1),-450ones(N,1)];
w6 = [400ones(N,1),-500ones(N,1)];

% 恶意干扰在移动
% 干扰点的初始轨迹
Jx1 =0 ;%圆心
Jy1 = 0;
Jw1 = 1000;%radius[0,0,200]
theta = 0:2pi/(N-1):2pi;
J11 = Jx1 - Jw1cos(theta);
J21 = Jy1 - Jw1
sin(theta);
J11 = J11’;
J21 = J21’;
J = [J11,J21];
pm=0.5;%1干扰功率
p1 = 1ones(N,1);%为什么当功率初始值为0.01时优化不可行
p2 = 1
ones(N,1);
p3 = 1ones(N,1);
p4 = 1
ones(N,1);
p5 = 1ones(N,1);
p6 = 1
ones(N,1);
pmax=6;
B=10;
vmax = 40; %maximum speed
beta_db = -60; %单位距离下信道增益 dB
sigma_db = -148; %噪声强度dB
beta = 10^(beta_db/10)10^7;
sigma = 10^(sigma_db/10)10^7;
H = 100; %无人机高度
%初始轨迹 初始化不同,轨迹也不同
q1=[700
ones(N/6,1),100
ones(N/6,1);
200ones(N/6,1),450ones(N/6,1);
-300ones(N/6,1),450ones(N/6,1);
-600ones(N/6,1),200ones(N/6,1);
-450ones(N/6,1),-450ones(N/6,1);
400ones(N/6,1),-500ones(N/6,1)];
q1s=q1;
%无人机与用户位置d_r,u
for cntTra=1:1:1
d11 = H^2+(q1s(:,1)-w1(:,1)).^2+(q1s(:,2)-w1(:,2)).^2;
d12 = H^2+(q1s(:,1)-w2(:,1)).^2+(q1s(:,2)-w2(:,2)).^2;
d13 = H^2+(q1s(:,1)-w3(:,1)).^2+(q1s(:,2)-w3(:,2)).^2;
d14 = H^2+(q1s(:,1)-w4(:,1)).^2+(q1s(:,2)-w4(:,2)).^2;
d15 = H^2+(q1s(:,1)-w5(:,1)).^2+(q1s(:,2)-w5(:,2)).^2;
d16 = H^2+(q1s(:,1)-w6(:,1)).^2+(q1s(:,2)-w6(:,2)).^2;
d= H^2+(q1s(:,1)-J(:,1)).^2+(q1s(:,2)-J(:,2)).^2;

Is=sigma+(beta.*pm)./d;%初始可行解
Ls1=d11./(beta.*p1);
Ls2=d12./(beta.*p2);
Ls3=d13./(beta.*p3);
Ls4=d14./(beta.*p4);
Ls5=d15./(beta.*p5);
Ls6=d16./(beta.*p6);
LIs1=Ls1.*Is;
LIs2=Ls2.*Is;
LIs3=Ls3.*Is;
LIs4=Ls4.*Is;
LIs5=Ls5.*Is;
LIs6=Ls6.*Is;

A1=-B./(Ls1+Ls1.^2.*Is);%泰勒展开系数
A2=-B./(Ls2+Ls2.^2.*Is);
A3=-B./(Ls3+Ls3.^2.*Is);
A4=-B./(Ls4+Ls4.^2.*Is);
A5=-B./(Ls5+Ls5.^2.*Is);
A6=-B./(Ls6+Ls6.^2.*Is);

C1=-B./(Is+Is.^2.*Ls1);%泰勒展开系数
C2=-B./(Is+Is.^2.*Ls2);
C3=-B./(Is+Is.^2.*Ls3);
C4=-B./(Is+Is.^2.*Ls4);
C5=-B./(Is+Is.^2.*Ls5);
C6=-B./(Is+Is.^2.*Ls6);

cvx_begin

variable q1(N,2)
variable L1(N)
variable L2(N)
variable L3(N)
variable L4(N)
variable L5(N)
variable L6(N)
variable I(N)

variable eta
variable m(N)

expression qu1(N,1)

maximize eta

subject to

B.log(1+1./(LIs1))+A1.(L1(:)-Ls1(:))+C1.(I(:)-Is(:))>=eta;%(1-1)
B.log(1+1./(LIs2))+A2.(L2(:)-Ls2(:))+C2.
(I(:)-Is(:))>=eta;
B.log(1+1./(LIs3))+A3.(L3(:)-Ls3(:))+C3.(I(:)-Is(:))>=eta;
B.log(1+1./(LIs4))+A4.(L4(:)-Ls4(:))+C4.
(I(:)-Is(:))>=eta;
B.log(1+1./(LIs5))+A5.(L5(:)-Ls5(:))+C5.(I(:)-Is(:))>=eta;
B.log(1+1./(LIs6))+A6.(L6(:)-Ls6(:))+C6.
(I(:)-Is(:))>=eta;

% sum(B.log(1+1./(LIs1))+A1.(L1(:)-Ls1(:))+C1.(I(:)-Is(:)))/T>=eta;%(1-1)
% sum(B.log(1+1./(LIs2))+A2.(L2(:)-Ls2(:))+C2.
(I(:)-Is(:)))/T>=eta;
% sum(B.log(1+1./(LIs3))+A3.(L3(:)-Ls3(:))+C3.(I(:)-Is(:)))/T>=eta;
% sum(B.log(1+1./(LIs4))+A4.(L4(:)-Ls4(:))+C4.
(I(:)-Is(:)))/T>=eta;
% sum(B.log(1+1./(LIs5))+A5.(L5(:)-Ls5(:))+C5.(I(:)-Is(:)))/T>=eta;
% sum(B.log(1+1./(LIs6))+A6.(L6(:)-Ls6(:))+C6.
(I(:)-Is(:)))/T>=eta;

for i=1:1:N

H^2+sum_square_abs(q1(i,:)-w1(i,:))<=beta.*p1.*L1(i)%(1-2)
H^2+sum_square_abs(q1(i,:)-w2(i,:))<=beta.*p2.*L2(i);
H^2+sum_square_abs(q1(i,:)-w3(i,:))<=beta.*p3.*L3(i);
H^2+sum_square_abs(q1(i,:)-w4(i,:))<=beta.*p4.*L4(i);
H^2+sum_square_abs(q1(i,:)-w5(i,:))<=beta.*p5.*L5(i);
H^2+sum_square_abs(q1(i,:)-w6(i,:))<=beta.*p6.*L6(i);

pm*beta.*inv_pos(m(i))+sigma<=I(i)%(1-3)

end

for i=1:1:N
qu1(i)=2*q1s(i,1)q1(i,1)-q1s(i,1)^2+J(i,1)^2-2J(i,1)q1(i,1)+2q1s(i,2)q1(i,2)-q1s(i,2)^2+J(i,2)^2-2J(i,2)*q1(i,2)+H^2;%一阶泰勒
end

m<=qu1;%1-4
m>=0;%(1-5)

for i = 1:1:N-1
norm(q1(i+1,:)-q1(i,:))<=vmax*delta;
end

cvx_end

q1s=q1;
optvalue1(cntTra) = cvx_optval;
figure(1)
plot(optvalue1,‘r-o’);

end

%plot(q1(:,1),q1(:,2),‘r-’);

Calling Mosek 9.1.9: 2756 variables, 1080 equality constraints
For improved efficiency, Mosek is solving the dual problem.

MOSEK Version 9.1.9 (Build date: 2019-11-21 11:34:40)
Copyright © MOSEK ApS, Denmark. WWW: mosek.com
Platform: Windows/64-X86

Problem
Name :
Objective sense : min
Type : CONIC (conic optimization problem)
Constraints : 1080
Cones : 479
Scalar variables : 2756
Matrix variables : 0
Integer variables : 0

Optimizer started.
Presolve started.
Linear dependency checker started.
Linear dependency checker terminated.
Eliminator started.
Freed constraints in eliminator : 0
Eliminator terminated.
Eliminator - tries : 1 time : 0.00
Lin. dep. - tries : 1 time : 0.01
Lin. dep. - number : 0
Presolve terminated. Time: 0.03
Problem
Name :
Objective sense : min
Type : CONIC (conic optimization problem)
Constraints : 1080
Cones : 479
Scalar variables : 2756
Matrix variables : 0
Integer variables : 0

Optimizer - threads : 8
Optimizer - solved problem : the primal
Optimizer - Constraints : 601
Optimizer - Cones : 479
Optimizer - Scalar variables : 2277 conic : 1797
Optimizer - Semi-definite variables: 0 scalarized : 0
Factor - setup time : 0.00 dense det. time : 0.00
Factor - ML order time : 0.00 GP order time : 0.00
Factor - nonzeros before factor : 2697 after factor : 2969
Factor - dense dim. : 0 flops : 3.26e+04
ITE PFEAS DFEAS GFEAS PRSTATUS POBJ DOBJ MU TIME
0 1.0e+00 5.8e+06 3.9e+07 0.00e+00 3.930236000e+07 0.000000000e+00 1.0e+00 0.06
1 4.7e-02 2.8e+05 8.5e+06 -1.00e+00 3.926778523e+07 -5.221317987e-02 4.7e-02 0.14
2 4.1e-03 2.4e+04 2.5e+06 -9.99e-01 3.901433535e+07 -3.988400743e-01 4.1e-03 0.16
3 2.9e-04 1.7e+03 6.5e+05 -9.92e-01 3.752611447e+07 -1.073565499e+00 2.9e-04 0.16
4 5.3e-05 3.1e+02 2.6e+05 -9.47e-01 3.450462535e+07 2.921887290e+00 5.3e-05 0.16
5 2.1e-05 1.2e+02 1.5e+05 -8.60e-01 3.225669753e+07 1.508471947e+01 2.1e-05 0.16
6 4.6e-06 2.7e+01 5.6e+04 -7.70e-01 2.393110550e+07 1.130122906e+02 4.6e-06 0.16
7 9.7e-07 5.7e+00 1.3e+04 -3.71e-01 1.092616789e+07 4.707947942e+02 9.7e-07 0.17
8 1.1e-07 6.3e-01 5.5e+02 4.72e-01 1.508185680e+06 9.312244432e+02 1.1e-07 0.17
9 4.5e-10 2.6e-03 1.3e-01 9.71e-01 7.454090559e+03 1.024101107e+03 4.5e-10 0.17
10 1.2e-10 7.1e-04 2.3e-02 8.10e-01 1.965048004e+03 -5.667036478e+01 1.2e-10 0.19
11 1.6e-11 9.4e-05 8.3e-04 1.00e+00 -3.979996617e+02 -6.513590036e+02 1.6e-11 0.19
12 1.6e-11 9.4e-05 8.3e-04 1.28e+00 -3.979996617e+02 -6.513590036e+02 1.6e-11 0.19
13 1.6e-11 9.4e-05 8.3e-04 1.22e+00 -3.979996617e+02 -6.513590036e+02 1.6e-11 0.20
Optimizer terminated. Time: 0.23

Interior-point solution summary
Problem status : DUAL_INFEASIBLE
Solution status : DUAL_INFEASIBLE_CER
Primal. obj: -9.8181814249e-04 nrm: 9e-01 Viol. con: 3e-06 var: 7e-09 cones: 0e+00
Optimizer summary
Optimizer - time: 0.23
Interior-point - iterations : 14 time: 0.20
Basis identification - time: 0.00
Primal - iterations : 0 time: 0.00
Dual - iterations : 0 time: 0.00
Clean primal - iterations : 0 time: 0.00
Clean dual - iterations : 0 time: 0.00
Simplex - time: 0.00
Primal simplex - iterations : 0 time: 0.00
Dual simplex - iterations : 0 time: 0.00
Mixed integer - relaxations: 0 time: 0.00


Status: Infeasible
Optimal value (cvx_optval): -Inf

I tried to solve it with sdpt3, but the data will become negative, and the optimized result is far less than the initial value. Such optimization is also unreasonable and failed

Mosek says your model is infeasible and the certificate is of reasonable but not extremely good quality. So it is possible that your model is borderline between feasible/infeasible, which usually makes solvers struggle. Since your previous log showed that Mosek stopped with a humongous solution norm of 4e+12, it is also possible that the solution you are looking for is so far out that the solvers cannot reach it without going into numerical problems first.