python
复制代码
import optimtool as oo
from optimtool.base import np, sp, plt
python
复制代码
pip install optimtool>=2.5.0
python
复制代码
import optimtool.hybrid as oh
oh.[方法名].[函数名]([目标函数], [参数表], [初始迭代点], [正则化参数], [邻近算子名])
ϕ ( x ) = f ( x ) + h ( x ) \phi(x) = f(x) + h(x) ϕ(x)=f(x)+h(x)
其中 f ( x ) f(x) f(x)是可微的。 h ( x ) h(x) h(x)不是可微的,并且具备简单的形式。optimtool.hybrid能够选择的 h ( x ) h(x) h(x)有: ∣ ∣ x ∣ ∣ 1 ||x||1 ∣∣x∣∣1, ∣ ∣ x ∣ ∣ 2 ||x||2 ∣∣x∣∣2, − ∑ i ln ( x i ) -\sum {i}{\ln(x_i)} −∑iln(xi),实例:
f ( x ) = ∑ i = 1 n ( ( n − ∑ j = 1 n cos x j ) + i ( 1 − cos x i ) − sin x i ) 2 , x 0 = [ 0.2 , 0.2 , . . . , 0.2 ] f(x)=\sum {i=1}^{n}((n-\sum_{j=1}^{n}\cos x_j)+i(1-\cos x_i)-\sin x_i)^2, x_0=[0.2, 0.2, ...,0.2] f(x)=i=1∑n((n−j=1∑ncosxj)+i(1−cosxi)−sinxi)2,x0=[0.2,0.2,...,0.2]
python
复制代码
import optimtool.hybrid as oh
x = sp.symbols("x1:3")
f = (2 - (sp.cos(x[0]) + sp.cos(x[1])) + (1 - sp.cos(x[0])) - sp.sin(x[0]))**2 + \
(2 - (sp.cos(x[0]) + sp.cos(x[1])) + 2 * (1 - sp.cos(x[1])) - sp.sin(x[1]))**2
x_0 = (0.2, 0.2) # Random given
近似点算法(approt)
python
复制代码
oh.approt.[函数名]([目标函数], [参数表], [初始迭代点], [正则化参数], [邻近算子名])
方法头
解释
grad(funcs: FuncArray, args: ArgArray, x_0: PointArray, mu: float=1e-3, proxim: str="L1", tk: float=0.02, verbose: bool=False, draw: bool=True, output_f: bool=False, epsilon: float=1e-6, k: int=0) -> OutputType
基于梯度方法的邻近近似
python
复制代码
oh.approt.grad(f, x, x_0, verbose=True, epsilon=1e-4)
text
复制代码
(0.2, 0.2) 0.033830304000793295 0
[0.19925643 0.19925643] 0.03371630105707655 1
[0.19849759 0.19849759] 0.033599113758384015 2
[0.19772323 0.19772323] 0.0334786576252087 3
[0.19693311 0.19693311] 0.03335484671764522 4
[0.19612697 0.19612697] 0.03322759368760871 5
[0.19530458 0.19530458] 0.03309680983956681 6
[0.19446568 0.19446568] 0.032962405200405005 7
[0.19361004 0.19361004] 0.03282428859906577 8
[0.19273741 0.19273741] 0.03268236775663266 9
[0.19184754 0.19184754] 0.03253654938754205 10
[0.19094021 0.19094021] 0.03238673931263206 11
[0.19001517 0.19001517] 0.03223284258474419 12
[0.18907219 0.18907219] 0.032074763627614224 13
[0.18811105 0.18811105] 0.031912406388790386 14
[0.18713151 0.18713151] 0.03174567450732931 15
[0.18613337 0.18613337] 0.03157447149700892 16
[0.1851164 0.1851164] 0.03139870094579865 17
[0.18408039 0.18408039] 0.031218266732314086 18
[0.18302515 0.18302515] 0.03103307325995774 19
[0.18195048 0.18195048] 0.03084302570942143 20
[0.18085618 0.18085618] 0.030648030310189818 21
[0.17974209 0.17974209] 0.03044799463163445 22
[0.17860802 0.17860802] 0.030242827894225312 23
[0.17745382 0.17745382] 0.030032441301325655 24
[0.17627935 0.17627935] 0.029816748391938645 25
[0.17508445 0.17508445] 0.029595665414685356 26
[0.17386901 0.17386901] 0.029369111723178076 27
[0.17263291 0.17263291] 0.029137010192819283 28
[0.17137605 0.17137605] 0.028899287658918093 29
[0.17009835 0.17009835] 0.028655875375845567 30
[0.16879974 0.16879974] 0.02840670949677072 31
[0.16748017 0.16748017] 0.02815173157332011 32
[0.1661396 0.1661396] 0.02789088907428339 33
[0.16477802 0.16477802] 0.027624135922245694 34
[0.16339543 0.16339543] 0.02735143304677512 35
[0.16199186 0.16199186] 0.02707274895251694 36
[0.16056734 0.16056734] 0.026788060300246677 37
[0.15912196 0.15912196] 0.026497352498637897 38
[0.15765579 0.15765579] 0.02620062030415762 39
[0.15616897 0.15616897] 0.025897868426188044 40
[0.15466162 0.15466162] 0.02558911213410671 41
[0.15313393 0.15313393] 0.025274377862715594 42
[0.15158608 0.15158608] 0.024953703812053064 43
[0.15001829 0.15001829] 0.024627140537261723 44
[0.14843083 0.14843083] 0.02429475152384472 45
[0.14682398 0.14682398] 0.023956613743302973 46
[0.14519804 0.14519804] 0.023612818183825806 47
[0.14355337 0.14355337] 0.023263470350419237 48
[0.14189035 0.14189035] 0.02290869072858788 49
[0.14020939 0.14020939] 0.022548615205470916 50
[0.13851092 0.13851092] 0.022183395442150533 51
[0.13679544 0.13679544] 0.02181319919073435 52
[0.13506345 0.13506345] 0.021438210549756025 53
[0.13331551 0.13331551] 0.021058630151454174 54
[0.13155219 0.13155219] 0.020674675274577677 55
[0.12977412 0.12977412] 0.02028657987655194 56
[0.12798195 0.12798195] 0.01989459453910834 57
[0.12617636 0.12617636] 0.019498986321851795 58
[0.12435809 0.12435809] 0.01910003851871226 59
[0.12252788 0.12252788] 0.01869805031281132 60
[0.12068653 0.12068653] 0.01829333632594862 61
[0.11883487 0.11883487] 0.01788622605971978 62
[0.11697374 0.11697374] 0.01747706322615784 63
[0.11510403 0.11510403] 0.017066204966793598 64
[0.11322666 0.11322666] 0.016654020960107936 65
[0.11134258 0.11134258] 0.016240892418513574 66
[0.10945276 0.10945276] 0.01582721097723825 67
[0.10755818 0.10755818] 0.015413377478760959 68
[0.10565989 0.10565989] 0.01499980065777625 69
[0.10375891 0.10375891] 0.014586895732992659 70
[0.1018563 0.1018563] 0.014175082913403667 71
[0.09995316 0.09995316] 0.013764785827967332 72
[0.09805056 0.09805056] 0.013356429888878502 73
[0.09614962 0.09614962] 0.012950440599784873 74
[0.09425145 0.09425145] 0.01254724182137033 75
[0.09235718 0.09235718] 0.012147254007661526 76
[0.09046792 0.09046792] 0.011750892427213808 77
[0.08858482 0.08858482] 0.011358565383947966 78
[0.08670899 0.08670899] 0.01097067245284757 79
[0.08484156 0.08484156] 0.010587602745955627 80
[0.08298365 0.08298365] 0.010209733224125174 81
[0.08113635 0.08113635] 0.009837427069776122 82
[0.07930076 0.07930076] 0.009471032135477047 83
[0.07747796 0.07747796] 0.009110879482523235 84
[0.07566899 0.07566899] 0.008757282022810418 85
[0.07387489 0.07387489] 0.008410533276236762 86
[0.07209667 0.07209667] 0.008070906254602977 87
[0.07033531 0.07033531] 0.007738652481558527 88
[0.06859176 0.06859176] 0.007414001156570364 89
[0.06686694 0.06686694] 0.007097158469209606 90
[0.06516172 0.06516172] 0.0067883070682847125 91
[0.06347696 0.06347696] 0.006487605688524489 92
[0.06181345 0.06181345] 0.006195188935681534 93
[0.06017198 0.06017198] 0.005911167229092196 94
[0.05855326 0.05855326] 0.005635626898953724 95
[0.05695796 0.05695796] 0.005368630433870282 96
[0.05538674 0.05538674] 0.005110216872624309 97
[0.05384017 0.05384017] 0.004860402332652406 98
[0.0523188 0.0523188] 0.004619180666398659 99
[0.05082312 0.05082312] 0.004386524235562022 100
[0.04935359 0.04935359] 0.004162384792297757 101
[0.0479106 0.0479106] 0.003946694455662684 102
[0.04649451 0.04649451] 0.003739366771024543 103
[0.04510562 0.04510562] 0.0035402978397843406 104
[0.0437442 0.0437442] 0.0033493675065847757 105
[0.04241045 0.04241045] 0.0031664405911896133 106
[0.04110454 0.04110454] 0.0029913681524116327 107
[0.0398266 0.0398266] 0.002823988771812003 108
[0.0385767 0.0385767] 0.0026641298453995883 109
[0.03735488 0.03735488] 0.0025116088721840162 110
[0.03616115 0.03616115] 0.0023662347291712976 111
[0.03499544 0.03499544] 0.002227808923221081 112
[0.03385768 0.03385768] 0.002096126811077554 113
[0.03274776 0.03274776] 0.001970978779829355 114
[0.03166552 0.03166552] 0.0018521513810318142 115
[0.03061077 0.03061077] 0.0017394284127048485 116
[0.0295833 0.0295833] 0.0016325919444068876 117
[0.02858286 0.02858286] 0.0015314232815397497 118
[0.02760917 0.02760917] 0.001435703865969758 119
[0.02666194 0.02666194] 0.0013452161109320454 120
[0.02574084 0.02574084] 0.0012597441690125495 121
[0.02484552 0.02484552] 0.001179074632770029 122
[0.02397562 0.02397562] 0.001102997168258484 123
[0.02313076 0.02313076] 0.0010313050823431134 124
[0.02231052 0.02231052] 0.0009637958252554067 125
[0.0215145 0.0215145] 0.0009002714303206027 126
[0.02074227 0.02074227] 0.0008405388932003743 127
[0.01999338 0.01999338] 0.0007844104933347607 128
[0.01926738 0.01926738] 0.0007317040605428159 129
[0.01856381 0.01856381] 0.0006822431899514119 130
[0.01788221 0.01788221] 0.0006358574085730946 131
[0.01722209 0.01722209] 0.0005923822969516482 132
[0.01658299 0.01658299] 0.0005516595693425212 133
[0.01596441 0.01596441] 0.0005135371158985923 134
[0.01536588 0.01536588] 0.00047786901029846864 135
[0.01478692 0.01478692] 0.0004445154861861646 136
[0.01422703 0.01422703] 0.0004133428856947098 137
[0.01368574 0.01368574] 0.0003842235832073654 138
[0.01316255 0.01316255] 0.0003570358873697328 139
[0.01265701 0.01265701] 0.00033166392421483536 140
[0.01216862 0.01216862] 0.0003079975040960318 141
[0.01169692 0.01169692] 0.00028593197495247396 142
[0.01124144 0.01124144] 0.00026536806425459664 143
[0.01080172 0.01080172] 0.0002462117117987103 144
[0.01037731 0.01037731] 0.00022837389534282435 145
[0.00996775 0.00996775] 0.00021177045090091796 146
[0.00957262 0.00957262] 0.0001963218893420302 147
[0.00919146 0.00919146] 0.00018195321077671775 148
[0.00882387 0.00882387] 0.000168593718055502 149
[0.00846941 0.00846941] 0.00015617683055352998 150
[0.00812768 0.00812768] 0.0001446398992749004 151
[0.00779828 0.00779828] 0.0001339240241768505 152
[0.00748082 0.00748082] 0.00012397387449037303 153
[0.0071749 0.0071749] 0.000114737512699174 154
[0.00688016 0.00688016] 0.00010616622273352944 155
[0.00659622 0.00659622] 9.821434283851575e-05 156
[0.00632274 0.00632274] 9.08391034888525e-05 157
[0.00605935 0.00605935] 8.400047064208593e-05 158
[0.00580573 0.00580573] 7.766099455120823e-05 159
[0.00556154 0.00556154] 7.17856642930692e-05 160
[0.00532645 0.00532645] 6.63417681125835e-05 161
[0.00510016 0.00510016] 6.129875963233148e-05 162
[0.00488236 0.00488236] 5.6628129933378544e-05 163
[0.00467276 0.00467276] 5.23032854751192e-05 164
[0.00447106 0.00447106] 4.829943178895962e-05 165
[0.00427699 0.00427699] 4.459346285271314e-05 166
[0.00409028 0.00409028] 4.116385602937666e-05 167
[0.00391066 0.00391066] 3.799057243413012e-05 168
[0.00373789 0.00373789] 3.505496257755243e-05 169
[0.00357171 0.00357171] 3.2339677120730474e-05 170
[0.0034119 0.0034119] 2.982858256774279e-05 171
[0.0032582 0.0032582] 2.7506681714183043e-05 172
[0.00311041 0.00311041] 2.5360038665186197e-05 173
[0.00296831 0.00296831] 2.337570823368496e-05 174
[0.00283169 0.00283169] 2.154166952822405e-05 175
[0.00270034 0.00270034] 1.9846763539975257e-05 176
[0.00257407 0.00257407] 1.8280634540178872e-05 177
[0.00245269 0.00245269] 1.6833675101623048e-05 178
[0.00233602 0.00233602] 1.549697456151136e-05 179
[0.00222389 0.00222389] 1.4262270747130963e-05 180
[0.00211612 0.00211612] 1.3121904790621107e-05 181
[0.00201254 0.00201254] 1.206877886445294e-05 182
[0.00191301 0.00191301] 1.1096316674962034e-05 183
[0.00181737 0.00181737] 1.0198426557197656e-05 184
[0.00172546 0.00172546] 9.36946702044605e-06 185
[0.00163716 0.00163716] 8.60421460020984e-06 186
[0.00155231 0.00155231] 7.897833878607748e-06 187
[0.0014708 0.0014708] 7.245849541505941e-06 188
[0.00139249 0.00139249] 6.644120347093789e-06 189
[0.00131725 0.00131725] 6.088814886589323e-06 190
[0.00124498 0.00124498] 5.576389024080719e-06 191
[0.00117555 0.00117555] 5.103564908254004e-06 192
[0.00110886 0.00110886] 4.667311454629785e-06 193
[0.0010448 0.0010448] 4.26482620251418e-06 194
[0.00098327 0.00098327] 3.89351845623686e-06 195
[0.00092417 0.00092417] 3.5509936254760143e-06 196
[0.00086741 0.00086741] 3.2350386844359184e-06 197
[0.00081289 0.00081289] 2.9436086744619057e-06 198
[0.00076053 0.00076053] 2.6748141791812922e-06 199
[0.00071025 0.00071025] 2.4269097056574906e-06 200
[0.00066196 0.00066196] 2.1982829091729086e-06 201
[0.00061559 0.00061559] 1.9874446031613262e-06 202
[0.00057106 0.00057106] 1.7930194995873036e-06 203
[0.00052829 0.00052829] 1.613737628526414e-06 204
[0.00048723 0.00048723] 1.4484263891181658e-06 205
[0.0004478 0.0004478] 1.2960031871400677e-06 206
[0.00040993 0.00040993] 1.1554686174483923e-06 207
[0.00037357 0.00037357] 1.025900152341892e-06 208
[0.00033867 0.00033867] 9.064462994919141e-07 209
[0.00030515 0.00030515] 7.963211955915816e-07 210
[0.00027296 0.00027296] 6.947996041620743e-07 211
[0.00024206 0.00024206] 6.01212288158414e-07 212
[0.00021239 0.00021239] 5.14941730029167e-07 213
[0.00018391 0.00018391] 4.3541817380762416e-07 214
[0.00015656 0.00015656] 3.621159655820409e-07 215
[0.0001303 0.0001303] 2.9455017038155757e-07 216
[0.0001051 0.0001051] 2.3227344505032461e-07 217
[8.08945057e-05 8.08945057e-05] 1.748731481504906e-07 218
[5.76602959e-05 5.76602959e-05] 1.2196866929042707e-07 219
[3.53546819e-05 3.53546819e-05] 7.320896153659465e-08 220
text
复制代码
(array([3.53546819e-05, 3.53546819e-05]), 220)
FISTA算法(fista)
python
复制代码
oh.fista.[函数名]([目标函数], [参数表], [初始迭代点], [正则化参数], [邻近算子名])
方法头
解释
normal(funcs: FuncArray, args: ArgArray, x_0: PointArray, mu: float=1e-3, proxim: str="L1", tk: float=0.02, verbose: bool=False, draw: bool=True, output_f: bool=False, epsilon: float=1e-6, k: int=0) -> OutputType
两步计算一个新点
variant(funcs: FuncArray, args: ArgArray, x_0: PointArray, mu: float=1e-3, proxim: str="L1", tk: float=0.02, verbose: bool=False, draw: bool=True, output_f: bool=False, epsilon: float=1e-6, k: int=0) -> OutputType
normal法的等价变形
decline(funcs: FuncArray, args: ArgArray, x_0: PointArray, mu: float=1e-3, proxim: str="L1", tk: float=0.02, verbose: bool=False, draw: bool=True, output_f: bool=False, epsilon: float=1e-6, k: int=0) -> OutputType
基于函数下降趋势的变体
python
复制代码
oh.fista.normal(f, x, x_0, verbose=True, epsilon=1e-4)
text
复制代码
(0.2, 0.2) 0.033830304000793295 0
[0.19925643 0.19925643] 0.03371630105707655 1
[0.19849759 0.19849759] 0.033599113758384015 2
[0.19752965 0.19752965] 0.03344840822740475 3
[0.19634058 0.19634058] 0.03326140386972337 4
[0.19491599 0.19491599] 0.03303467660717308 5
[0.19323907 0.19323907] 0.03276408584834668 6
[0.19129038 0.19129038] 0.03244469143441093 7
[0.18904782 0.18904782] 0.03207066253319544 8
[0.18648645 0.18648645] 0.03163518232735821 9
[0.18357846 0.18357846] 0.031130355220309 10
[0.18029315 0.18029315] 0.03054712756229315 11
[0.17659697 0.17659697] 0.02987523906813377 12
[0.17245376 0.17245376] 0.029103230710925534 13
[0.16782507 0.16782507] 0.028218546457533245 14
[0.16267078 0.16267078] 0.02720778105084813 15
[0.15695006 0.15695006] 0.02605714379032019 16
[0.15062263 0.15062263] 0.02475322722482501 17
[0.1436507 0.1436507] 0.023284185711105593 18
[0.13600142 0.13600142] 0.02164143378200551 19
[0.12765019 0.12765019] 0.019821954357666566 20
[0.11858478 0.11858478] 0.017831241286453643 21
[0.10881027 0.10881027] 0.01568676317570666 22
[0.09835481 0.09835481] 0.01342160091785943 23
[0.08727586 0.08727586] 0.01108757276749394 24
[0.0756663 0.0756663] 0.008756758818819103 25
[0.06365966 0.06365966] 0.006519996602965627 26
[0.05143318 0.05143318] 0.004480874877680786 27
[0.03920737 0.03920737] 0.0027443108743679394 28
[0.0272407 0.0272407] 0.0014001940854970719 29
[0.01581839 0.01581839] 0.0005047242448837863 30
[0.00523537 0.00523537] 6.4288746819883e-05 31
[-0.00418665 -0.00418665] 4.3944790650174575e-05 32
[-0.01221648 -0.01221648] 0.0003358041809499229 33
[-0.01868558 -0.01868558] 0.000782018176888592 34
[-0.02349934 -0.02349934] 0.001243963960329223 35
[-0.02664181 -0.02664181] 0.001608014900872538 36
[-0.02817331 -0.02817331] 0.0018038384618738578 37
[-0.02822152 -0.02822152] 0.001810201114210672 38
[-0.02696779 -0.02696779] 0.0016486746491471192 39
[-0.02463074 -0.02463074] 0.0013692472198611092 40
[-0.02144946 -0.02144946] 0.001033310326812164 41
[-0.01766789 -0.01766789] 0.0006987912458611627 42
[-0.01352189 -0.01352189] 0.0004102193974182509 43
[-0.00922928 -0.00922928] 0.0001943611548287423 44
[-0.00498303 -0.00498303] 6.0496796608013615e-05 45
[-0.00094732 -0.00094732] 3.6954413794614425e-06 46
[0.00270463 0.00270463] 1.9901129925390336e-05 47
[0.00587664 0.00587664] 7.94091094216332e-05 48
[0.00850264 0.00850264] 0.00015732135728718983 49
[0.01054374 0.01054374] 0.00023529252758100567 50
[0.01198517 0.01198517] 0.00029932348310261373 51
[0.0128333 0.0128333] 0.00034041104566886213 52
[0.01311286 0.01311286] 0.0003545027009827691 53
[0.01286437 0.01286437] 0.0003419638510109866 54
[0.01214176 0.01214176] 0.0003067200850132879 55
[0.01100998 0.01100998] 0.00025519907496466464 56
[0.0095427 0.0095427] 0.00019517498982886958 57
[0.00781981 0.00781981] 0.00013461228868992265 58
[0.00592486 0.00592486] 8.060873795925052e-05 59
[0.00394224 0.00394224] 3.853955135280601e-05 60
[0.00195435 0.00195435] 1.149547685501415e-05 61
[3.86693674e-05 3.86693674e-05] 8.032897007816521e-08 62
[-0.00169515 -0.00169515] 9.17148721695113e-06 63
[-0.00319296 -0.00319296] 2.7004334602874763e-05 64
[-0.00441392 -0.00441392] 4.8397238412822366e-05 65
[-0.00533136 -0.00533136] 6.857481635587594e-05 66
[-0.00593302 -0.00593302] 8.373629979419888e-05 67
[-0.0062205 -0.0062205] 9.15235913915823e-05 68
[-0.0062082 -0.0062082] 9.118306879630649e-05 69
[-0.00592161 -0.00592161] 8.34343820763833e-05 70
[-0.0053953 -0.0053953] 7.01133065277566e-05 71
[-0.00467068 -0.00467068] 5.368776346101515e-05 72
[-0.00379356 -0.00379356] 3.6752630190872105e-05 73
[-0.00281189 -0.00281189] 2.1593233635829933e-05 74
[-0.00177358 -0.00177358] 9.877481468752762e-06 75
[-0.00072459 -0.00072459] 2.501921465760068e-06 76
[0.00025271 0.00025271] 6.330306539194115e-07 77
[0.00112552 0.00112552] 4.7746896689738605e-06 78
[0.00186791 0.00186791] 1.0668443587263032e-05 79
[0.00246108 0.00246108] 1.6931895980111533e-05 80
[0.00289345 0.00289345] 2.2361878461997472e-05 81
[0.00316041 0.00316041] 2.6076758170315184e-05 82
[0.00326391 0.00326391] 2.7591230673255495e-05 83
[0.00321191 0.00321191] 2.682517662881479e-05 84
[0.00301765 0.00301765] 2.405576611892222e-05 85
[0.00269879 0.00269879] 1.9827263265911833e-05 86
[0.00227655 0.00227655] 1.4835994988390612e-05 87
[0.00177463 0.00177463] 9.808812559984457e-06 88
[0.00121826 0.00121826] 5.392209040060288e-06 89
[0.00063314 0.00063314] 2.0662326628037452e-06 90
[4.4412847e-05 4.4412847e-05] 9.277008269260652e-08 91
[-0.00048426 -0.00048426] 1.4383293148952407e-06 92
[-0.00093604 -0.00093604] 3.6301580781364767e-06 93
[-0.00129827 -0.00129827] 5.9828727580486845e-06 94
[-0.00156272 -0.00156272] 8.03638649505745e-06 95
[-0.00172564 -0.00172564] 9.442956816861173e-06 96
[-0.00178759 -0.00178759] 1.000614664689354e-05 97
[-0.00175314 -0.00175314] 9.691006528007353e-06 98
[-0.0016304 -0.0016304] 8.607546021477958e-06 99
[-0.00143044 -0.00143044] 6.973690459175214e-06 100
[-0.00116663 -0.00116663] 5.066443040807669e-06 101
[-0.00085394 -0.00085394] 3.170660486780329e-06 102
[-0.0005082 -0.0005082] 1.5338563099619648e-06 103
[-0.00014544 -0.00014544] 3.33212610205221e-07 104
[0.00017878 0.00017878] 4.214390667731358e-07 105
[0.00045421 0.00045421] 1.3203776625245732e-06 106
[0.00067324 0.00067324] 2.250832733219641e-06 107
[0.00083097 0.00083097] 3.038967555581645e-06 108
[0.00092526 0.00092526] 3.557218652962131e-06 109
[0.00095658 0.00095658] 3.737100302233256e-06 110
[0.0009278 0.0009278] 3.5716314819327603e-06 111
[0.00084399 0.00084399] 3.1084058923828957e-06 112
[0.00071203 0.00071203] 2.4354998352661786e-06 113
[0.00054025 0.00054025] 1.6631435338735005e-06 114
[0.00033803 0.00033803] 9.043319121947985e-07 115
[0.00011537 0.00011537] 2.573406355634432e-07 116
[-7.75729405e-05 -7.75729405e-05] 1.671842709922099e-07 117
[-0.00023501 -0.00023501] 5.805689223087464e-07 118
[-0.00035294 -0.00035294] 9.55330129339356e-07 119
[-0.00042919 -0.00042919] 1.2273575895614658e-06 120
[-0.00046338 -0.00046338] 1.3569078800428007e-06 121
[-0.00045682 -0.00045682] 1.3316868956475513e-06 122
[-0.00041237 -0.00041237] 1.1653229296710031e-06 123
[-0.0003342 -0.0003342] 8.920309869145119e-07 124
[-0.00022756 -0.00022756] 5.587686146122985e-07 125
[-9.85008103e-05 -9.85008103e-05] 2.1641313021020493e-07 126
[6.43207414e-06 6.43207414e-06] 1.2946889566740209e-08 127
text
复制代码
(array([6.43207414e-06, 6.43207414e-06]), 127)
Nesterov算法(nesterov)
python
复制代码
oh.nesterov.[函数名]([目标函数], [参数表], [初始迭代点], [正则化参数], [邻近算子名])
方法头
解释
seckin(funcs: FuncArray, args: ArgArray, x_0: PointArray, mu: float=1e-3, proxim: str="L1", tk: float=0.02, verbose: bool=False, draw: bool=True, output_f: bool=False, epsilon: float=1e-6, k: int=0) -> OutputType
第二类Nesterov加速法
accer(funcs: FuncArray, args: ArgArray, x_0: PointArray, mu: float=1e-3, proxim: str="L1", lk: float=0.01, tk: float=0.02, verbose: bool=False, draw: bool=True, output_f: bool=False, epsilon: float=1e-6, k: int=0) -> OutputType
复合优化算法的加速框架
text
复制代码
(0.2, 0.2) 0.033830304000793295 0
[0.19925643 0.19925643] 0.03371630105707655 1
[0.19873208 0.19873208] 0.033635416161269645 2
[0.19824625 0.19824625] 0.033560113282182814 3
[0.19779193 0.19779193] 0.03348937977771688 4
[0.1973583 0.1973583] 0.03342158708171633 5
[0.19693596 0.19693596] 0.03335529505960039 6
[0.19651836 0.19651836] 0.03328949344326663 7
[0.19610149 0.19610149] 0.03322355499940229 8
[0.19568304 0.19568304] 0.033157115543780515 9
[0.19526174 0.19526174] 0.033089970332591104 10
[0.19483691 0.19483691] 0.033022004801464444 11
[0.19440817 0.19440817] 0.032953153342586375 12
[0.19397531 0.19397531] 0.03288337625764423 13
[0.19353821 0.19353821] 0.032812647334081416 14
[0.1930968 0.1930968] 0.03274094729563452 15
[0.19265102 0.19265102] 0.03266826039819621 16
[0.19220083 0.19220083] 0.032594572677139846 17
[0.19174619 0.19174619] 0.032519871051184146 18
[0.19128706 0.19128706] 0.03244414286707404 19
[0.19082343 0.19082343] 0.032367375670024706 20
[0.19035525 0.19035525] 0.0322895570894612 21
[0.1898825 0.1898825] 0.03221067478356757 22
[0.18940514 0.18940514] 0.03213071641385224 23
[0.18892316 0.18892316] 0.03204966963507849 24
[0.18843651 0.18843651] 0.031967522093123764 25
[0.18794517 0.18794517] 0.03188426142698723 26
[0.18744912 0.18744912] 0.03179987527304022 27
[0.18694832 0.18694832] 0.031714351270546136 28
[0.18644275 0.18644275] 0.03162767706796477 29
[0.18593238 0.18593238] 0.03153984032980098 30
[0.18541719 0.18541719] 0.03145082874386781 31
[0.18489715 0.18489715] 0.03136063002891287 32
[0.18437222 0.18437222] 0.03126923194257793 33
[0.1838424 0.1838424] 0.031176622289681582 34
[0.18330765 0.18330765] 0.03108278893081914 35
[0.18276794 0.18276794] 0.03098771979128425 36
[0.18222326 0.18222326] 0.03089140287031154 37
[0.18167359 0.18167359] 0.030793826250645626 38
[0.18111889 0.18111889] 0.030694978108438714 39
[0.18055915 0.18055915] 0.03059484672348252 40
[0.17999435 0.17999435] 0.030493420489773625 41
[0.17942446 0.17942446] 0.030390687926420742 42
[0.17884946 0.17884946] 0.03028663768889194 43
[0.17826935 0.17826935] 0.030181258580608418 44
[0.17768409 0.17768409] 0.030074539564881035 45
[0.17709367 0.17709367] 0.02996646977719818 46
[0.17649807 0.17649807] 0.029857038537858628 47
[0.17589728 0.17589728] 0.02974623536495526 48
[0.17529127 0.17529127] 0.02963404998770624 49
[0.17468005 0.17468005] 0.029520472360134945 50
[0.17406359 0.17406359] 0.029405492675093462 51
[0.17344188 0.17344188] 0.029289101378632255 52
[0.1728149 0.1728149] 0.029171289184710302 53
[0.17218266 0.17218266] 0.029052047090239855 54
[0.17154513 0.17154513] 0.028931366390468057 55
[0.17090231 0.17090231] 0.028809238694680148 56
[0.17025419 0.17025419] 0.028685655942228945 57
[0.16960076 0.16960076] 0.028560610418873568 58
[0.16894202 0.16894202] 0.028434094773425688 59
[0.16827797 0.16827797] 0.028306102034691773 60
[0.1676086 0.1676086] 0.02817662562869971 61
[0.16693391 0.16693391] 0.028045659396202398 62
[0.1662539 0.1662539] 0.027913197610440834 63
[0.16556856 0.16556856] 0.0277792349951582 64
[0.16487791 0.16487791] 0.027643766742844578 65
[0.16418194 0.16418194] 0.027506788533203375 66
[0.16348066 0.16348066] 0.02736829655181286 67
[0.16277408 0.16277408] 0.02722828750897477 68
[0.1620622 0.1620622] 0.027086758658721198 69
[0.16134503 0.16134503] 0.02694370781796281 70
[0.16062258 0.16062258] 0.026799133385756447 71
[0.15989486 0.15989486] 0.026653034362663773 72
[0.1591619 0.1591619] 0.026505410370182444 73
[0.15842369 0.15842369] 0.026356261670214508 74
[0.15768027 0.15768027] 0.026205589184552146 75
[0.15693165 0.15693165] 0.026053394514344092 76
[0.15617784 0.15617784] 0.02589967995951506 77
[0.15541887 0.15541887] 0.025744448538108148 78
[0.15465477 0.15465477] 0.02558770400550724 79
[0.15388556 0.15388556] 0.025429450873514402 80
[0.15311126 0.15311126] 0.0252696944292389 81
[0.15233191 0.15233191] 0.02510844075376142 82
[0.15154753 0.15154753] 0.0249456967405326 83
[0.15075817 0.15075817] 0.024781470113468172 84
[0.14996385 0.14996385] 0.024615769444693614 85
[0.14916461 0.14916461] 0.02444860417189907 86
[0.14836049 0.14836049] 0.024279984615258984 87
[0.14755153 0.14755153] 0.02410992199386683 88
[0.14673778 0.14673778] 0.023938428441643678 89
[0.14591927 0.14591927] 0.02376551702266892 90
[0.14509606 0.14509606] 0.023591201745887268 91
[0.14426819 0.14426819] 0.02341549757913816 92
[0.14343572 0.14343572] 0.023238420462463397 93
[0.1425987 0.1425987] 0.02305998732063617 94
[0.14175718 0.14175718] 0.022880216074865917 95
[0.14091122 0.14091122] 0.022699125653621477 96
[0.14006088 0.14006088] 0.022516736002528296 97
[0.13920622 0.13920622] 0.022333068093279024 98
[0.13834731 0.13834731] 0.022148143931513993 99
[0.13748421 0.13748421] 0.02196198656361456 100
[0.136617 0.136617] 0.021774620082362042 101
[0.13574573 0.13574573] 0.02158606963140784 102
[0.13487049 0.13487049] 0.02139636140851093 103
[0.13399135 0.13399135] 0.02120552266748679 104
[0.13310839 0.13310839] 0.021013581718826876 105
[0.13222168 0.13222168] 0.020820567928938712 106
[0.13133131 0.13133131] 0.020626511717963182 107
[0.13043736 0.13043736] 0.02043144455612627 108
[0.12953992 0.12953992] 0.020235398958584165 109
[0.12863908 0.12863908] 0.02003840847872382 110
[0.12773492 0.12773492] 0.01984050769987993 111
[0.12682754 0.12682754] 0.019641732225440522 112
[0.12591704 0.12591704] 0.019442118667302975 113
[0.1250035 0.1250035] 0.019241704632659312 114
[0.12408704 0.12408704] 0.019040528709081284 115
[0.12316775 0.12316775] 0.018838630447888367 116
[0.12224573 0.12224573] 0.018636050345776825 117
[0.12132109 0.12132109] 0.018432829824700743 118
[0.12039394 0.12039394] 0.01822901120999248 119
[0.11946438 0.11946438] 0.01802463770671741 120
[0.11853253 0.11853253] 0.017819753374264793 121
[0.1175985 0.1175985] 0.017614403099174197 122
[0.11666241 0.11666241] 0.017408632566209822 123
[0.11572436 0.11572436] 0.01720248822769215 124
[0.11478448 0.11478448] 0.016996017271108478 125
[0.11384289 0.11384289] 0.016789267585023703 126
[0.11289972 0.11289972] 0.016582287723316894 127
[0.11195507 0.11195507] 0.01637512686778442 128
[0.11100909 0.11100909] 0.016167834789139506 129
[0.11006189 0.11006189] 0.015960461806458145 130
[0.1091136 0.1091136] 0.015753058745119824 131
[0.10816436 0.10816436] 0.015545676893292247 132
[0.10721428 0.10721428] 0.015338367957028396 133
[0.10626351 0.10626351] 0.015131184014033131 134
[0.10531218 0.10531218] 0.014924177466172134 135
[0.10436042 0.10436042] 0.014717400990799457 136
[0.10340836 0.10340836] 0.014510907490978745 137
[0.10245615 0.10245615] 0.014304750044688593 138
[0.10150391 0.10150391] 0.014098981853094075 139
[0.10055179 0.10055179] 0.013893656187982946 140
[0.09959992 0.09959992] 0.01368882633845935 141
[0.09864844 0.09864844] 0.013484545556998294 142
[0.09769749 0.09769749] 0.013280867004964954 143
[0.09674722 0.09674722] 0.013077843697704697 144
[0.09579776 0.09579776] 0.01287552844931507 145
[0.09484925 0.09484925] 0.01267397381721372 146
[0.09390183 0.09390183] 0.012473232046613741 147
[0.09295564 0.09295564] 0.012273355015029202 148
[0.09201083 0.09201083] 0.012074394176923015 149
[0.09106754 0.09106754] 0.011876400508619371 150
[0.09012589 0.09012589] 0.011679424453601839 151
[0.08918605 0.08918605] 0.011483515868314823 152
[0.08824814 0.08824814] 0.01128872396858924 153
[0.0873123 0.0873123] 0.011095097276812349 154
[0.08637867 0.08637867] 0.010902683569959014 155
[0.0854474 0.0854474] 0.010711529828601291 156
[0.08451862 0.08451862] 0.01052168218701202 157
[0.08359246 0.08359246] 0.010333185884471796 158
[0.08266906 0.08266906] 0.01014608521789343 159
[0.08174856 0.08174856] 0.00996042349586558 160
[0.08083108 0.08083108] 0.009776242994221208 161
[0.07991677 0.07991677] 0.009593584913228696 162
[0.07900575 0.07900575] 0.009412489336499564 163
[0.07809816 0.07809816] 0.009232995191701765 164
[0.07719411 0.07719411] 0.009055140213166078 165
[0.07629375 0.07629375] 0.008878960906460193 166
[0.07539718 0.07539718] 0.008704492515007505 167
[0.07450455 0.07450455] 0.008531768988817391 168
[0.07361596 0.07361596] 0.008360822955386055 169
[0.07273153 0.07273153] 0.008191685692826979 170
[0.0718514 0.0718514] 0.008024387105275749 171
[0.07097566 0.07097566] 0.007858955700613185 172
[0.07010445 0.07010445] 0.007695418570540102 173
[0.06923786 0.06923786] 0.007533801373033395 174
[0.068376 0.068376] 0.007374128317203251 175
[0.06751899 0.06751899] 0.007216422150565665 176
[0.06666693 0.06666693] 0.007060704148737249 177
[0.06581992 0.06581992] 0.006906994107553427 178
[0.06497806 0.06497806] 0.00675531033760118 179
[0.06414146 0.06414146] 0.006605669661154516 180
[0.06331019 0.06331019] 0.006458087411491039 181
[0.06248436 0.06248436] 0.006312577434564489 182
[0.06166406 0.06166406] 0.006169152092997068 183
[0.06084938 0.06084938] 0.006027822272356063 184
[0.06004039 0.06004039] 0.005888597389665743 185
[0.05923718 0.05923718] 0.005751485404107095 186
[0.05843983 0.05843983] 0.005616492829845886 187
[0.05764842 0.05764842] 0.005483624750930617 188
[0.05686301 0.05686301] 0.0053528848381917 189
[0.05608369 0.05608369] 0.00522427536807383 190
[0.05531051 0.05531051] 0.005097797243323587 191
[0.05454354 0.05454354] 0.00497345001545756 192
[0.05378285 0.05378285] 0.00485123190892543 193
[0.05302849 0.05302849] 0.004731139846886088 194
[0.05228051 0.05228051] 0.004613169478506701 195
[0.05153898 0.05153898] 0.004497315207696919 196
[0.05080394 0.05080394] 0.004383570223184056 197
[0.05007544 0.05007544] 0.004271926529837656 198
[0.04935352 0.04935352] 0.0041623749811483205 199
[0.04863823 0.04863823] 0.004054905312763828 200
[0.0479296 0.0479296] 0.003949506176988748 201
[0.04722767 0.04722767] 0.003846165178149841 202
[0.04653248 0.04653248] 0.003744868908731468 203
[0.04584405 0.04584405] 0.00364560298618641 204
[0.0451624 0.0451624] 0.003548352090325825 205
[0.04448758 0.04448758] 0.00345310000119703 206
[0.04381958 0.04381958] 0.0033598296373552653 207
[0.04315845 0.04315845] 0.003268523094439369 208
[0.04250418 0.04250418] 0.0031791616839638233 209
[0.04185681 0.04185681] 0.003091725972240079 210
[0.04121632 0.04121632] 0.0030061958193431815 211
[0.04058275 0.04058275] 0.002922550418043142 212
[0.03995608 0.03995608] 0.0028407683326218945 213
[0.03933633 0.03933633] 0.002760827537500723 214
[0.0387235 0.0387235] 0.002682705455604866 215
[0.03811758 0.03811758] 0.002606378996397809 216
[0.03751857 0.03751857] 0.0025318245935165348 217
[0.03692646 0.03692646] 0.0024590182419483536 218
[0.03634126 0.03634126] 0.0023879355346878837 219
[0.03576293 0.03576293] 0.002318551698819148 220
[0.03519148 0.03519148] 0.0022508416309721096 221
[0.03462689 0.03462689] 0.0021847799321037384 222
[0.03406913 0.03406913] 0.0021203409415594945 223
[0.0335182 0.0335182] 0.0020574987703744028 224
[0.03297407 0.03297407] 0.0019962273337760157 225
[0.03243671 0.03243671] 0.0019365003828540328 226
[0.03190611 0.03190611] 0.001878291535368826 227
[0.03138223 0.03138223] 0.001821574305668823 228
[0.03086505 0.03086505] 0.0017663221336947984 229
[0.03035453 0.03035453] 0.0017125084130502617 230
[0.02985065 0.02985065] 0.0016601065181208472 231
[0.02935337 0.02935337] 0.0016090898302279768 232
[0.02886266 0.02886266] 0.0015594317628068093 233
[0.02837848 0.02837848] 0.0015111057855995852 234
[0.02790078 0.02790078] 0.0014640854478586128 235
[0.02742955 0.02742955] 0.0014183444005574958 236
[0.02696472 0.02696472] 0.0013738564176089946 237
[0.02650626 0.02650626] 0.001330595416092694 238
[0.02605413 0.02605413] 0.001288535475496316 239
[0.02560829 0.02560829] 0.001247650855978515 240
[0.02516868 0.02516868] 0.0012079160156600926 241
[0.02473526 0.02473526] 0.0011693056269562992 242
[0.02430798 0.02430798] 0.0011317945919609778 243
[0.0238868 0.0238868] 0.0010953580568974848 244
[0.02347166 0.02347166] 0.0010599714256517984 245
[0.02306252 0.02306252] 0.001025610372404541 246
[0.02265933 0.02265933] 0.0009922508533802834 247
[0.02226202 0.02226202] 0.0009598691177337821 248
[0.02187056 0.02187056] 0.0009284417175927542 249
[0.02148488 0.02148488] 0.0008979455172794812 250
[0.02110493 0.02110493] 0.000868357701732434 251
[0.02073067 0.02073067] 0.0008396557841512164 252
[0.02036202 0.02036202] 0.0008118176128884495 253
[0.01999895 0.01999895] 0.0007848213776115862 254
[0.01964139 0.01964139] 0.0007586456147596944 255
[0.01928928 0.01928928] 0.0007332692123195464 256
[0.01894257 0.01894257] 0.0007086714139457371 257
[0.01860121 0.01860121] 0.0006848318224496608 258
[0.01826513 0.01826513] 0.0006617304026828131 259
[0.01793428 0.01793428] 0.0006393474838384433 260
[0.01760859 0.01760859] 0.0006176637611976787 261
[0.01728803 0.01728803] 0.0005966602973436125 262
[0.01697251 0.01697251] 0.0005763185228690176 263
[0.01666199 0.01666199] 0.0005566202366011656 264
[0.01635641 0.01635641] 0.0005375476053680995 265
[0.01605571 0.01605571] 0.0005190831633299191 266
[0.01575982 0.01575982] 0.0005012098108984406 267
[0.01546871 0.01546871] 0.0004839108132677776 268
[0.01518229 0.01518229] 0.0004671697985785609 269
[0.01490052 0.01490052] 0.0004509707557372565 270
[0.01462335 0.01462335] 0.00043529803191223943 271
[0.0143507 0.0143507] 0.0004201363297273299 272
[0.01408252 0.01408252] 0.00040547070417281143 273
[0.01381876 0.01381876] 0.0003912865592539738 274
[0.01355935 0.01355935] 0.0003775696443959911 275
[0.01330425 0.01330425] 0.0003643060506235078 276
[0.01305339 0.01305339] 0.00035148220653310216 277
[0.01280671 0.01280671] 0.0003390848740755047 278
[0.01256417 0.01256417] 0.0003271011441643905 279
[0.0123257 0.0123257] 0.000315518432127678 280
[0.01209124 0.01209124] 0.00030432447301666937 281
[0.01186075 0.01186075] 0.0002935073167876891 282
[0.01163416 0.01163416] 0.00028305532337051406 283
[0.01141143 0.01141143] 0.0002729571576369438 284
[0.01119249 0.01119249] 0.000263201784282491 285
[0.01097729 0.01097729] 0.00025377846263343733 286
[0.01076579 0.01076579] 0.00024467674139096147 287
[0.01055792 0.01055792] 0.0002358864533235877 288
[0.01035363 0.01035363] 0.0002273977099183539 289
[0.01015287 0.01015287] 0.0002192008960007555 290
[0.00995559 0.00995559] 0.00021128666433314824 291
[0.00976174 0.00976174] 0.00020364593020011297 292
[0.00957126 0.00957126] 0.00019626986598967865 293
[0.00938411 0.00938411] 0.00018914989577801418 294
[0.00920023 0.00920023] 0.00018227768992514276 295
[0.00901957 0.00901957] 0.00017564515968864142 296
[0.00884209 0.00884209] 0.00016924445186189735 297
[0.00866774 0.00866774] 0.0001630679434428663 298
[0.00849646 0.00849646] 0.00015710823633906235 299
[0.00832821 0.00832821] 0.00015135815211410862 300
[0.00816295 0.00816295] 0.00014581072678047656 301
[0.00800062 0.00800062] 0.00014045920564314938 302
[0.00784118 0.00784118] 0.0001352970381981069 303
[0.00768459 0.00768459] 0.00013031787308957817 304
[0.00753079 0.00753079] 0.00012551555312920109 305
[0.00737975 0.00737975] 0.00012088411038074003 306
[0.00723141 0.00723141] 0.00011641776131247725 307
[0.00708575 0.00708575] 0.00011211090202042497 308
[0.0069427 0.0069427] 0.0001079581035241948 309
[0.00680224 0.00680224] 0.00010395410713774198 310
[0.00666431 0.00666431] 0.00010009381991663805 311
[0.00652888 0.00652888] 9.637231018341094e-05 312
[0.0063959 0.0063959] 9.278480313214474e-05 313
[0.00626534 0.00626534] 8.932667651360674e-05 314
[0.00613715 0.00613715] 8.599345640158698e-05 315
[0.0060113 0.0060113] 8.27808130411733e-05 316
[0.00588774 0.00588774] 7.96845567797724e-05 317
[0.00576644 0.00576644] 7.670063408066092e-05 318
[0.00564737 0.00564737] 7.382512361986245e-05 319
[0.00553047 0.00553047] 7.105423246593916e-05 320
[0.00541572 0.00541572] 6.838429234300977e-05 321
[0.00530307 0.00530307] 6.581175597650488e-05 322
[0.0051925 0.0051925] 6.333319352139349e-05 323
[0.00508397 0.00508397] 6.094528907257019e-05 324
[0.00497744 0.00497744] 5.864483725688187e-05 325
[0.00487287 0.00487287] 5.64287399059034e-05 326
[0.00477024 0.00477024] 5.4294002809103636e-05 327
[0.00466952 0.00466952] 5.223773254648501e-05 328
[0.00457066 0.00457066] 5.025713339975743e-05 329
[0.00447363 0.00447363] 4.8349504341251034e-05 330
[0.00437841 0.00437841] 4.65122360995673e-05 331
[0.00428496 0.00428496] 4.4742808300888244e-05 332
[0.00419325 0.00419325] 4.3038786684965573e-05 333
[0.00410325 0.00410325] 4.1397820394612525e-05 334
[0.00401493 0.00401493] 3.9817639337442446e-05 335
[0.00392826 0.00392826] 3.829605161899657e-05 336
[0.00384322 0.00384322] 3.683094104558913e-05 337
[0.00375976 0.00375976] 3.542026469609136e-05 338
[0.00367787 0.00367787] 3.406205056101091e-05 339
[0.00359752 0.00359752] 3.275439524784383e-05 340
[0.00351868 0.00351868] 3.149546175131387e-05 341
[0.00344132 0.00344132] 3.028347728723066e-05 342
[0.00336541 0.00336541] 2.9116731188578645e-05 343
[0.00329094 0.00329094] 2.7993572862673044e-05 344
[0.00321787 0.00321787] 2.6912409807924855e-05 345
[0.00314617 0.00314617] 2.587170568900249e-05 346
[0.00307583 0.00307583] 2.486997846903073e-05 347
[0.00300682 0.00300682] 2.3905798597549508e-05 348
[0.00293912 0.00293912] 2.2977787252856655e-05 349
[0.0028727 0.0028727] 2.20846146376197e-05 350
[0.00280753 0.00280753] 2.1224998326240244e-05 351
[0.00274361 0.00274361] 2.0397701662919066e-05 352
[0.00268089 0.00268089] 1.9601532208991935e-05 353
[0.00261937 0.00261937] 1.883534023843161e-05 354
[0.00255901 0.00255901] 1.809801728017769e-05 355
[0.0024998 0.0024998] 1.7388494706163496e-05 356
[0.00244172 0.00244172] 1.670574236376479e-05 357
[0.00238475 0.00238475] 1.6048767251580275e-05 358
[0.00232886 0.00232886] 1.5416612237305273e-05 359
[0.00227403 0.00227403] 1.480835481659927e-05 360
[0.00222026 0.00222026] 1.4223105911786891e-05 361
[0.00216751 0.00216751] 1.366000870937832e-05 362
[0.00211576 0.00211576] 1.311823753518719e-05 363
[0.00206501 0.00206501] 1.2596996766101033e-05 364
[0.00201523 0.00201523] 1.2095519777400712e-05 365
[0.0019664 0.0019664] 1.1613067924637512e-05 366
[0.0019185 0.0019185] 1.1148929559048495e-05 367
[0.00187152 0.00187152] 1.0702419075553807e-05 368
[0.00182545 0.00182545] 1.0272875992311643e-05 369
[0.00178025 0.00178025] 9.859664061011676e-06 370
[0.00173593 0.00173593] 9.462170406882034e-06 371
[0.00169245 0.00169245] 9.079804697563365e-06 372
[0.00164981 0.00164981] 8.711998339975418e-06 373
[0.00160799 0.00160799] 8.358203704318186e-06 374
[0.00156698 0.00156698] 8.017893374369995e-06 375
[0.00152675 0.00152675] 7.690559423282887e-06 376
[0.0014873 0.0014873] 7.375712714069477e-06 377
[0.00144861 0.00144861] 7.072882224024897e-06 378
[0.00141066 0.00141066] 6.781614392332616e-06 379
[0.00137344 0.00137344] 6.501472490098185e-06 380
[0.00133695 0.00133695] 6.232036012135668e-06 381
[0.00130115 0.00130115] 5.972900089786467e-06 382
[0.00126605 0.00126605] 5.723674924106858e-06 383
[0.00123162 0.00123162] 5.4839852387632465e-06 384
[0.00119786 0.00119786] 5.253469752014746e-06 385
[0.00116476 0.00116476] 5.031780667124716e-06 386
[0.00113229 0.00113229] 4.8185831806585625e-06 387
[0.00110045 0.00110045] 4.61355500800892e-06 388
[0.00106923 0.00106923] 4.416385925655004e-06 389
[0.00103861 0.00103861] 4.2267773295451844e-06 390
[0.00100858 0.00100858] 4.044441809096762e-06 391
[0.00097913 0.00097913] 3.869102736298022e-06 392
[0.00095026 0.00095026] 3.700493869380984e-06 393
[0.00092194 0.00092194] 3.5383589705979077e-06 394
[0.00089418 0.00089418] 3.3824514376269443e-06 395
[0.00086695 0.00086695] 3.232533948133618e-06 396
[0.00084025 0.00084025] 3.0883781170478396e-06 397
[0.00081407 0.00081407] 2.94976416613777e-06 398
[0.00078839 0.00078839] 2.8164806054292237e-06 399
[0.00076322 0.00076322] 2.6883239261085353e-06 400
[0.00073853 0.00073853] 2.5650983044683457e-06 401
[0.00071432 0.00071432] 2.44661531656334e-06 402
[0.00069059 0.00069059] 2.3326936631592584e-06 403
[0.00066731 0.00066731] 2.2231589046593196e-06 404
[0.00064449 0.00064449] 2.11784320563874e-06 405
[0.00062211 0.00062211] 2.016585088657386e-06 406
[0.00060017 0.00060017] 1.919229197033664e-06 407
[0.00057865 0.00057865] 1.825626066258607e-06 408
[0.00055755 0.00055755] 1.7356319037491587e-06 409
[0.00053687 0.00053687] 1.6491083766527646e-06 410
[0.00051658 0.00051658] 1.5659224074112806e-06 411
[0.0004967 0.0004967] 1.4859459768213094e-06 412
[0.00047719 0.00047719] 1.4090559343083683e-06 413
[0.00045807 0.00045807] 1.335133815183569e-06 414
[0.00043932 0.00043932] 1.2640656646134436e-06 415
[0.00042094 0.00042094] 1.1957418680721542e-06 416
[0.00040292 0.00040292] 1.1300569880437374e-06 417
[0.00038524 0.00038524] 1.0669096067619877e-06 418
[0.00036791 0.00036791] 1.0062021747454344e-06 419
[0.00035092 0.00035092] 9.478408649510595e-07 420
[0.00033427 0.00033427] 8.917354323201652e-07 421
[0.00031793 0.00031793] 8.377990785361823e-07 422
[0.00030192 0.00030192] 7.85948321800158e-07 423
[0.00028621 0.00028621] 7.36102871446234e-07 424
[0.00027082 0.00027082] 6.881855072200287e-07 425
[0.00025572 0.00025572] 6.421219630515972e-07 426
[0.00024092 0.00024092] 5.978408151566175e-07 427
[0.00022641 0.00022641] 5.552733743151943e-07 428
[0.00021219 0.00021219] 5.143535821732855e-07 429
[0.00019824 0.00019824] 4.750179114116801e-07 430
[0.00018456 0.00018456] 4.372052696607062e-07 431
[0.00017115 0.00017115] 4.008569069992713e-07 432
[0.00015801 0.00015801] 3.65916326928599e-07 433
[0.00014512 0.00014512] 3.323292006813797e-07 434
[0.00013248 0.00013248] 3.0004328474480166e-07 435
[0.00012009 0.00012009] 2.690083414838671e-07 436
[0.00010794 0.00010794] 2.3917606274719583e-07 437
[9.60311213e-05 9.60311213e-05] 2.1049999634564198e-07 438
[8.43542062e-05 8.43542062e-05] 1.8293547530152638e-07 439
[7.29058658e-05 7.29058658e-05] 1.5643954975844092e-07 440
[6.16816554e-05 6.16816554e-05] 1.3097092146267022e-07 441
[5.06772157e-05 5.06772157e-05] 1.064898807130837e-07 442
[3.98882708e-05 3.98882708e-05] 8.29582456925748e-08 443
[2.93106273e-05 2.93106273e-05] 6.03393040914673e-08 444
text
复制代码
(array([2.93106273e-05, 2.93106273e-05]), 444)