如何解决Solver Gurobi和OSQP QP优化产生截然不同的结果
结果不同 对于相同规格的Gurobi求解器输出是正确的，而OSQP给出了错误的解决方案，这是可以解决的。为什么？ 下面是Gurobi障碍求解器日志
Parameter OutputFlag unchanged
Value: 1 Min: 0 Max: 1 Default: 1
Changed value of parameter QCPDual to 1
Prev: 0 Min: 0 Max: 1 Default: 0
Gurobi Optimizer version 9.0.0 build v9.0.0rc2 (win64)
Optimize a model with 240240 rows,77196 columns and 489172 nonzeros
Model fingerprint: 0xdb33ff46
Model has 56012 quadratic objective terms
Coefficient statistics:
Matrix range [5e-02,3e+00]
Objective range [3e-04,1e+04]
QObjective range [8e-11,4e-04]
Bounds range [0e+00,0e+00]
RHS range [3e-01,1e+04]
Presolve removed 239256 rows and 76677 columns
Presolve time: 0.26s
Presolved: 984 rows,519 columns,2643 nonzeros
Presolved model has 815 quadratic objective terms
Ordering time: 0.00s
Barrier statistics:
Free vars : 90
AA' NZ : 6.720e+03
Factor NZ : 1.221e+04 (roughly 1 MByte of memory)
Factor Ops : 2.632e+05 (less than 1 second per iteration)
Threads : 1
Objective Residual
Iter Primal Dual Primal Dual Compl Time
0 2.52540363e+06 -5.69095127e+06 2.52e+04 8.95e+02 9.05e+05 0s
1 9.93757272e+04 -3.17876909e+06 3.99e+03 1.41e+02 1.48e+05 0s
2 9.11301845e+04 -2.28607080e+06 1.51e+02 5.36e+00 7.27e+03 0s
3 1.03378792e+05 -3.92723265e+05 1.11e+01 3.95e-01 7.64e+02 0s
4 8.86245744e+04 -1.08269004e+05 2.03e+00 7.18e-02 2.00e+02 0s
5 3.53997941e+04 -2.58776169e+04 3.90e-01 1.38e-02 4.66e+01 0s
6 1.06137209e+04 -6.30710427e+03 6.09e-02 2.16e-03 1.11e+01 0s
7 3.52031557e+03 -2.17212662e+03 1.59e-02 5.64e-04 3.60e+00 0s
8 5.44622629e+02 -5.55752181e+02 1.92e-03 6.79e-05 6.80e-01 0s
9 5.52045072e+01 -2.14523216e+02 4.00e-04 1.42e-05 1.66e-01 0s
10 1.10554192e+01 -4.47823436e+01 1.59e-05 5.62e-07 3.42e-02 0s
11 1.51041071e+00 -1.18890369e+01 1.59e-11 4.47e-12 8.22e-03 0s
12 -1.08480431e+00 -4.01317984e+00 2.12e-12 5.28e-13 1.80e-03 0s
13 -1.45897649e+00 -2.93519651e+00 8.81e-13 1.49e-13 9.05e-04 0s
14 -1.76654941e+00 -2.25628749e+00 1.11e-14 4.28e-15 3.00e-04 0s
15 -1.85031146e+00 -1.90760537e+00 3.77e-15 2.87e-14 3.51e-05 0s
16 -1.86177425e+00 -1.87515813e+00 3.79e-14 1.05e-13 8.21e-06 0s
17 -1.86490849e+00 -1.86537066e+00 9.09e-14 4.35e-15 2.83e-07 0s
18 -1.86502277e+00 -1.86502331e+00 2.86e-13 1.31e-14 3.29e-10 0s
19 -1.86502290e+00 -1.86502290e+00 6.58e-13 7.11e-15 3.29e-13 0s
Barrier solved model in 19 iterations and 0.33 seconds
Optimal objective -1.86502290e+00
############################################
is_dcp: True
is_qp: True
status: optimal
objective_value: 1.8650229007993528
num_scalar_variables: 25788
num_scalar_data: 9929000
num_scalar_eq_constr: 17220
num_scalar_leq_constr 51660
max_data_dimension 205
solve_time: 0.3559989929199219
setup_time: None
num_iters: 19
############################################
The solution is: 1.8650229007993528
AxesSubplot(0.125,0.125;0.775x0.755)
**ECOS Solver log:**
ECOS 2.0.7 - (C) embotech GmbH,Zurich Switzerland,2012-15. Web: www.embotech.com/ECOS
It pcost dcost gap pres dres k/t mu step sigma IR | BT
0 +1.092e+09 -6.958e+10 +4e+12 8e-01 2e+00 1e+00 2e+07 --- --- 1 1 - | - -
1 +1.410e+09 -6.488e+10 +4e+12 7e-01 2e+00 3e+06 2e+07 0.2645 7e-01 1 1 1 | 0 0
2 +8.501e+08 -4.165e+10 +3e+12 4e-01 1e+00 7e+06 1e+07 0.4430 4e-01 1 1 1 | 0 0
3 +7.276e+08 -3.919e+10 +2e+12 4e-01 1e+00 1e+07 1e+07 0.2989 7e-01 1 1 1 | 0 0
4 +3.586e+08 -1.424e+10 +1e+12 1e-01 5e-01 7e+06 5e+06 0.5661 8e-02 1 1 1 | 0 0
5 +1.687e+08 -6.445e+09 +5e+11 5e-02 2e-01 3e+06 2e+06 0.6304 1e-01 1 1 1 | 0 0
6 -7.805e+06 -2.949e+09 +3e+11 2e-02 1e-01 9e+05 1e+06 0.5757 2e-01 1 1 1 | 0 0
7 +2.228e+06 -4.316e+08 +5e+10 3e-03 1e-02 8e+04 2e+05 0.8413 4e-02 1 1 1 | 0 0
8 +9.190e+06 -5.985e+07 +1e+10 5e-04 2e-03 8e+03 5e+04 0.8146 1e-02 1 1 1 | 0 0
9 +7.369e+06 -3.535e+07 +8e+09 3e-04 2e-03 5e+03 3e+04 0.3837 4e-01 1 1 1 | 0 0
10 +5.982e+06 -2.426e+07 +6e+09 2e-04 1e-03 3e+03 3e+04 0.2838 4e-01 1 1 1 | 0 0
11 +4.323e+06 -1.388e+07 +5e+09 1e-04 7e-04 2e+03 2e+04 0.4220 4e-01 1 1 1 | 0 0
12 +2.879e+06 -6.529e+06 +3e+09 5e-05 4e-04 7e+02 1e+04 0.3985 2e-01 1 1 1 | 0 0
13 +1.730e+06 -3.407e+06 +2e+09 3e-05 2e-04 3e+02 1e+04 0.4899 4e-01 1 1 1 | 0 0
14 +9.686e+05 -1.651e+06 +1e+09 1e-05 9e-05 1e+02 7e+03 0.5477 3e-01 1 1 1 | 0 0
15 +3.289e+05 -4.201e+05 +5e+08 3e-06 2e-05 4e+01 2e+03 0.6879 9e-02 1 1 1 | 0 0
16 +1.385e+05 -1.646e+05 +3e+08 1e-06 9e-06 1e+01 1e+03 0.6088 1e-01 1 1 1 | 0 0
17 +5.012e+04 -6.306e+04 +1e+08 4e-07 3e-06 4e+00 5e+02 0.6767 1e-01 1 1 1 | 0 0
18 +5.720e+03 -1.644e+04 +2e+07 7e-08 6e-07 6e-01 1e+02 0.8278 5e-02 1 1 1 | 0 0
19 +4.088e+03 -7.287e+03 +1e+07 3e-08 3e-07 3e-01 5e+01 0.6584 3e-01 1 1 1 | 0 0
20 +6.631e+02 -1.644e+03 +3e+06 7e-09 6e-08 5e-02 1e+01 0.8262 4e-02 1 1 1 | 0 0
21 +3.407e+02 -8.118e+02 +1e+06 3e-09 3e-08 2e-02 6e+00 0.6915 3e-01 1 1 1 | 0 0
22 +7.454e+01 -2.141e+02 +3e+05 8e-10 7e-09 5e-03 1e+00 0.7854 5e-02 1 1 1 | 0 0
23 +3.983e+01 -1.278e+02 +2e+05 5e-10 4e-09 3e-03 8e-01 0.6239 3e-01 1 1 1 | 0 0
24 +1.895e+01 -6.678e+01 +1e+05 2e-10 2e-09 1e-03 4e-01 0.5217 6e-02 1 1 1 | 0 0
25 +1.821e+01 -6.528e+01 +9e+04 2e-10 2e-09 1e-03 4e-01 0.0894 7e-01 1 1 1 | 0 0
26 +4.428e+00 -2.336e+01 +3e+04 8e-11 7e-10 4e-04 1e-01 0.7785 1e-01 1 1 1 | 0 0
27 +2.873e+00 -1.913e+01 +2e+04 6e-11 5e-10 3e-04 1e-01 0.4966 6e-01 1 1 1 | 0 0
28 -5.236e-01 -6.848e+00 +7e+03 2e-11 2e-10 8e-05 3e-02 0.7740 8e-02 1 1 1 | 0 0
29 -1.221e+00 -4.313e+00 +3e+03 9e-12 7e-11 4e-05 2e-02 0.6117 2e-01 1 1 1 | 0 0
30 -1.535e+00 -3.250e+00 +2e+03 5e-12 4e-11 2e-05 9e-03 0.9890 5e-01 1 1 1 | 0 0
31 -1.798e+00 -2.147e+00 +4e+02 1e-12 8e-12 4e-06 2e-03 0.9553 2e-01 1 1 1 | 0 0
32 -1.847e+00 -1.939e+00 +1e+02 3e-13 2e-12 1e-06 5e-04 0.8829 2e-01 1 1 1 | 0 0
33 -1.856e+00 -1.905e+00 +5e+01 1e-13 1e-12 6e-07 2e-04 0.7592 4e-01 1 1 1 | 0 0
34 -1.865e+00 -1.866e+00 +1e+00 4e-15 6e-13 2e-08 7e-06 0.9804 8e-03 1 1 1 | 0 0
35 -1.865e+00 -1.866e+00 +1e+00 3e-15 6e-13 1e-08 5e-06 0.4717 3e-01 1 1 1 | 0 0
36 -1.865e+00 -1.866e+00 +3e+00 3e-15 5e-12 1e-08 1e-05 0.9890 1e+00 1 1 1 | 0 0
37 -1.865e+00 -1.865e+00 +1e-01 7e-16 2e-13 4e-10 6e-07 0.9631 4e-04 1 1 1 | 0 0
38 -1.865e+00 -1.865e+00 +5e-03 2e-15 6e-13 1e-11 2e-08 0.9762 8e-04 1 1 1 | 0 0
39 -1.865e+00 -1.865e+00 +2e-03 1e-15 6e-13 2e-11 1e-08 0.9499 5e-01 1 1 1 | 0 0
40 -1.865e+00 -1.865e+00 +2e-04 5e-16 6e-13 2e-12 9e-10 0.9158 3e-03 2 1 1 | 0 0
41 -1.865e+00 -1.865e+00 +6e-05 1e-14 6e-13 3e-13 3e-10 0.9890 1e-01 2 1 1 | 0 0
42 -1.865e+00 -1.865e+00 +2e-04 3e-12 6e-13 3e-13 7e-10 0.9334 5e-01 2 1 1 | 0 0
43 -1.865e+00 -1.865e+00 +5e-04 3e-13 6e-13 1e-12 2e-09 0.9890 8e-01 2 1 1 | 0 0
44 -1.865e+00 -1.865e+00 +1e-05 4e-14 6e-13 6e-14 5e-11 0.9890 1e-02 2 1 1 | 0 0
45 -1.865e+00 -1.865e+00 +7e-04 4e-12 6e-13 1e-13 3e-09 0.9890 9e-01 3 1 1 | 0 0
46 -1.865e+00 -1.865e+00 +7e-06 7e-12 6e-13 2e-15 3e-11 0.9890 1e-04 3 1 1 | 0 0
47 -1.865e+00 -1.865e+00 +9e-04 2e-08 6e-13 8e-14 4e-09 0.9890 1e+00 6 3 2 | 0 0
Unreliable search direction detected,recovering best iterate (37) and stopping.
NUMERICAL PROBLEMS (reached feastol=2.2e-13,reltol=7.5e-02,abstol=1.4e-01).
Runtime: 21.312740 seconds.
**SCS Solver log**
----------------------------------------------------------------------------
SCS v2.0.2 - Splitting Conic Solver
(c) Brendan O'Donoghue,Stanford University,2012-2017
----------------------------------------------------------------------------
Lin-sys: sparse-indirect,nnz in A = 755622,CG tol ~ 1/iter^(2.00)
eps = 1.00e-04,alpha = 1.50,max_iters = 5000,normalize = 1,scale = 1.00
acceleration_lookback = -1,rho_x = 1.00e-03
Variables n = 77364,constraints m = 246492
Cones: primal zero / dual free vars: 17220
linear vars: 223020
soc vars: 6252,soc blks: 168
Setup time: 6.81e-02s
----------------------------------------------------------------------------
Iter | pri res | dua res | rel gap | pri obj | dua obj | kap/tau | time (s)
----------------------------------------------------------------------------
0| 2.58e+00 1.50e+00 1.00e+00 -1.81e+10 -1.81e+06 0.00e+00 4.87e-01
100| 6.76e-04 1.51e-03 1.00e+00 8.50e+04 -1.40e+06 3.45e-06 9.99e+00
200| 5.05e-04 1.07e-04 7.84e-01 -1.51e+05 -1.24e+06 6.62e-07 1.60e+01
300| 5.05e-04 9.74e-05 7.82e-01 -1.37e+05 -1.12e+06 6.59e-07 2.22e+01
400| 5.05e-04 8.81e-05 7.80e-01 -1.24e+05 -1.01e+06 6.57e-07 2.88e+01
500| 5.04e-04 7.96e-05 7.77e-01 -1.12e+05 -8.94e+05 6.55e-07 3.53e+01
600| 5.04e-04 7.43e-05 7.74e-01 -1.05e+05 -8.23e+05 3.39e-06 4.08e+01
700| 5.04e-04 6.98e-05 7.70e-01 -9.92e+04 -7.63e+05 1.37e-06 4.63e+01
800| 5.04e-04 6.55e-05 7.66e-01 -9.35e+04 -7.04e+05 6.50e-07 5.18e+01
900| 5.04e-04 6.13e-05 7.60e-01 -8.79e+04 -6.45e+05 1.36e-06 5.77e+01
1000| 5.04e-04 5.73e-05 7.54e-01 -8.22e+04 -5.86e+05 1.36e-06 6.41e+01
1100| 5.04e-04 5.33e-05 7.47e-01 -7.66e+04 -5.28e+05 6.46e-07 6.97e+01
1200| 5.03e-04 4.97e-05 7.38e-01 -7.12e+04 -4.71e+05 1.35e-06 7.54e+01
1300| 5.03e-04 4.61e-05 7.27e-01 -6.59e+04 -4.16e+05 6.44e-07 8.12e+01
1400| 5.02e-04 4.29e-05 7.13e-01 -6.10e+04 -3.64e+05 2.64e-06 8.77e+01
1500| 5.01e-04 3.99e-05 6.96e-01 -5.64e+04 -3.15e+05 1.35e-06 9.48e+01
1600| 4.91e-04 3.75e-05 6.82e-01 -5.05e+04 -2.67e+05 6.42e-07 1.03e+02
1700| 4.85e-04 3.21e-05 6.53e-01 -4.62e+04 -2.20e+05 6.41e-07 1.13e+02
1800| 4.56e-04 3.05e-05 6.25e-01 -4.01e+04 -1.74e+05 1.35e-06 1.23e+02
1900| 3.47e-04 2.90e-05 6.75e-01 -2.74e+04 -1.41e+05 2.63e-06 1.35e+02
2000| 3.24e-04 2.68e-05 6.72e-01 -2.32e+04 -1.18e+05 1.34e-06 1.46e+02
2100| 2.94e-04 3.04e-05 6.27e-01 -2.20e+04 -9.63e+04 6.40e-07 1.57e+02
2200| 2.26e-04 3.56e-05 7.00e-01 -1.41e+04 -7.97e+04 2.62e-06 1.71e+02
2300| 1.61e-04 4.84e-05 6.37e-01 -1.50e+04 -6.78e+04 6.40e-07 1.85e+02
2400| 1.50e-04 4.68e-05 9.55e-01 -1.36e+03 -5.94e+04 6.40e-07 1.98e+02
2500| 1.27e-04 1.56e-05 7.93e-01 -6.12e+03 -5.30e+04 6.40e-07 2.11e+02
2600| 1.05e-04 1.65e-05 7.92e-01 -5.50e+03 -4.74e+04 6.39e-07 2.23e+02
2700| 8.02e-05 1.61e-05 7.92e-01 -4.96e+03 -4.28e+04 2.62e-06 2.36e+02
2800| 7.19e-05 1.18e-05 8.26e-01 -3.75e+03 -3.94e+04 6.39e-07 2.49e+02
2900| 5.43e-05 1.60e-05 8.10e-01 -3.82e+03 -3.64e+04 6.39e-07 2.62e+02
3000| 5.18e-05 1.27e-05 8.27e-01 -3.20e+03 -3.38e+04 6.39e-07 2.74e+02
3100| 4.90e-05 9.01e-06 8.88e-01 -1.84e+03 -3.12e+04 2.62e-06 2.86e+02
3200| 4.67e-05 1.33e-05 1.00e+00 2.29e+02 -2.88e+04 6.39e-07 2.99e+02
3300| 2.45e-05 1.03e-05 9.68e-01 -4.46e+02 -2.71e+04 1.34e-06 3.12e+02
3400| 2.39e-05 7.46e-06 9.67e-01 -4.26e+02 -2.56e+04 6.39e-07 3.22e+02
3500| 2.35e-05 7.34e-06 9.70e-01 -3.66e+02 -2.41e+04 6.39e-07 3.31e+02
3600| 2.28e-05 7.35e-06 9.70e-01 -3.40e+02 -2.27e+04 3.32e-06 3.41e+02
3700| 2.17e-05 7.55e-06 9.69e-01 -3.38e+02 -2.14e+04 6.39e-07 3.50e+02
3800| 2.10e-05 7.20e-06 9.69e-01 -3.14e+02 -2.01e+04 6.39e-07 3.60e+02
3900| 2.07e-05 6.73e-06 9.72e-01 -2.64e+02 -1.89e+04 1.34e-06 3.69e+02
4000| 2.04e-05 6.68e-06 9.76e-01 -2.14e+02 -1.77e+04 6.39e-07 3.79e+02
4100| 2.01e-05 6.64e-06 9.82e-01 -1.50e+02 -1.66e+04 6.39e-07 3.88e+02
4200| 2.00e-05 6.84e-06 9.88e-01 -9.38e+01 -1.55e+04 6.38e-07 3.98e+02
4300| 1.97e-05 6.52e-06 9.94e-01 -4.40e+01 -1.44e+04 6.38e-07 4.08e+02
4400| 1.96e-05 6.40e-06 1.00e+00 1.60e+01 -1.34e+04 1.34e-06 4.17e+02
4500| 1.95e-05 6.33e-06 1.00e+00 8.05e+01 -1.24e+04 1.34e-06 4.26e+02
4600| 1.90e-05 6.55e-06 1.00e+00 1.37e+02 -1.15e+04 6.38e-07 4.37e+02
4700| 1.86e-05 6.44e-06 1.00e+00 2.06e+02 -1.06e+04 6.38e-07 4.47e+02
4800| 1.73e-05 6.24e-06 1.00e+00 2.34e+02 -9.78e+03 3.32e-06 4.58e+02
4900| 1.69e-05 5.58e-06 1.00e+00 2.82e+02 -9.04e+03 1.34e-06 4.68e+02
5000| 1.63e-05 6.44e-06 1.00e+00 3.04e+02 -8.39e+03 6.38e-07 4.78e+02
----------------------------------------------------------------------------
Status: Solved/Inaccurate
Hit max_iters,solution may be inaccurate
Timing: Solve time: 4.78e+02s
Lin-sys: avg # CG iterations: 9.44,avg solve time: 8.25e-02s
Cones: avg projection time: 5.84e-04s
Acceleration: avg step time: 0.00e+00s
----------------------------------------------------------------------------
Error metrics:
dist(s,K) = 2.0111e-11,dist(y,K*) = 0.0000e+00,s'y/|s||y| = -3.8323e-18
primal res: |Ax + s - b|_2 / (1 + |b|_2) = 1.6308e-05
dual res: |A'y + c|_2 / (1 + |c|_2) = 6.4380e-06
rel gap: |c'x + b'y| / (1 + |c'x| + |b'y|) = 9.9988e-01
----------------------------------------------------------------------------
c'x = 304.2367,-b'y = -8388.8258
============================================================================
############################################
is_dcp: True
is_qp: True
status: optimal_inaccurate
objective_value: -304.2366584084115
num_scalar_variables: 25788
num_scalar_data: 9929000
num_scalar_eq_constr: 17220
num_scalar_leq_constr 51660
max_data_dimension 205
solve_time: 478413.86694549426
setup_time: 68.10835877191816
num_iters: 5000
############################################
The solution is: -304.2366584084115
AxesSubplot(0.125,0.125;0.775x0.755)
**OSQP Solver log:**
-----------------------------------------------------------------
OSQP v0.5.0 - Operator Splitting QP Solver
(c) Bartolomeo Stellato,Goran Banjac
University of Oxford - Stanford University 2018
-----------------------------------------------------------------
problem: variables n = 77196,constraints m = 240240
nnz(P) + nnz(A) = 545184
settings: linear system solver = qdldl,eps_abs = 1.0e-05,eps_rel = 1.0e-05,eps_prim_inf = 1.0e-04,eps_dual_inf = 1.0e-04,rho = 1.00e-01 (adaptive),sigma = 1.00e-06,alpha = 1.60,max_iter = 10000
check_termination: on (interval 25),scaling: on,scaled_termination: off
warm start: on,polish: on
objective pri res dua res rho time
1 -5.7757e+06 7.16e+00 2.76e+03 1.00e-01 6.68e-01s
200 -2.6075e+01 9.47e-04 1.39e+01 1.00e-01 5.78e+00s
400 9.8155e-01 3.12e-04 3.45e+00 1.00e-01 1.10e+01s
600 1.3592e-01 5.90e-05 1.63e+00 1.00e-01 1.61e+01s
800 1.6026e-01 3.36e-05 3.55e-01 1.00e-01 2.13e+01s
1000 1.1418e-01 9.08e-06 2.29e-01 1.00e-01 2.65e+01s
1100 9.9919e-02 8.85e-06 6.92e-02 1.00e-01 2.91e+01s
status: solved
solution polish: unsuccessful
number of iterations: 1100
optimal objective: 0.0999
run time: 2.93e+01s
optimal rho estimate: 8.18e-02
############################################
is_dcp: True
is_qp: True
status: optimal
objective_value: -0.09991904812176824
num_scalar_variables: 25788
num_scalar_data: 9929000
num_scalar_eq_constr: 17220
num_scalar_leq_constr 51660
max_data_dimension 205
solve_time: 29.289220095079017
setup_time: None
num_iters: 1100
############################################
The solution is: -0.099919048121768
以下代码的快照：
'''
优化=演示（数据输入=数据输入）
＃'CVXOPT'，'ECOS'，'ECOS_BB'，'GLPK'，#'GLPK_MI','GUROBI','OSQP','SCS'
solution = optimization.solve(solver =
'Gurobi')
class Demo:
def __init__(self,datain):
# validate_inputs(datain)
self.inputs = datain
self.trading_vecs =
[cp.Variable(self.I) for j in
range(self.J)]
#some code
self.objective =
self._define_objective()
self.constraints =
self._define_constraints()
self.problem =
cp.Problem(objective=
cp.Maximize(self.objective),constraints=self.constraints)
# outputs populated by calling
#solve()
self.solution = None
self.size_metrics = None
self.solver_stats = None
self.h = None # holding vectors
self.l = None # CaR vectors
self.v = None # unsecured
funding vector
def solve(self,solver="CPLEX",verbose=True,**kwargs):
"""Solve the CVXPY problem and
update the instance variables:
solution : optimal
objective value
size_metrics : problem size
metrics
solver_stats : solver
statistics
h : holding
vectors,a J-long list of I-long numpy
arrays
l : CaR vectors,a J-long list of I-long numpy arrays
v : unsecured
funding vector,a J-long numpy array
Parameters
---------
solver : string,defaults to
'CPLEX'; if CPLEX is not available,'ECOS'
has also been tested
successfully,but it is slower
verbose : boolean,defaults to
True
kwargs : CVXPY solver-specific
parameters;
Returns
-------
float value of the optimal
objective
"""
self.solution =
self.problem.solve(solver=solver,verbose=verbose,**kwargs)
self.size_metrics =
self.problem.size_metrics
self.solver_stats =
self.problem.solver_stats
if verbose:
self._print_problem_stats()
self.h =
[np.array(self.holding_vecs[j].value) \
for j in
range(self.J)]
self.l =
[np.array(self.car_holding_vecs[j].value) \
for j in
range(self.J)]
self.v =
[np.array(self.unsec_funding_vec[j].value) \
for j in
range(self.J)]
return self.solution
def _define_constraints(self):
"""Define the CVXPY problem
constraints"""
prev_holding_vecs =
[self.inputs['backbook_holding_vec']] + \
[self.holding_vecs[j] for j in
range(self.J-1)]
x_equations = [self.holding_vecs[j]
== self.inputs['trade_rolling_mats'][j] @ \
self.trading_vecs[j] == 0 for j in
range(self.J)]
#some other code
constraints = x_equations + \
marketable_trading + \
no_short_position + \
no_neg_borrowing + \
no_discretionary_unwind + \
within_cash_budget + \
car_lower_bounds + \
car_upper_bounds + \
car_funding + \
size_upper_limits
return constraints
def _define_objective(self):
"""Define the CVXPY objective"""
post_transaction_cost_return =
list(map(lambda r,h,phi,u:
_dot_product(r,h) - phi(u),self.inputs['expected_return_vecs'],\
self.holding_vecs,\
self.trading_vecs))
#some code#
objectives.append（self.inputs ['discount_vec'] [j] * \ \
self.inputs['lambda_3'] *
car_unwind_transaction_cost[j] - \
external_funding_cost[j]))
return cp.sum(objectives)
'''
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