我正在尝试求解非线性方程组：
|s11|^2+|s21|^2+|s31|^2+|s41|^2 <= 1 |s21|^2+|s22|^2+|s32|^2+|s42|^2 <= 1 |s31|^2+|s32|^2+|s33|^2+|s43|^2 <= 1 |s41|^2+|s42|^2+|s43|^2+|s44|^2 <= 1 ((s11 * (1 - (s43 * Γ2 * s43 * Γ1 + s33 * Γ1 + s44 * Γ2) (s33 * Γ1 * s44 * Γ2)) + s31 * Γ1 * s31 * (1-(s44 * Γ2)) + s31 * Γ1 * s43 * Γ2 * s41 + s41 * Γ2 * s41 * (1-( s33 * Γ1)) + s41 * Γ2 * s43 * Γ1 * s31) / (1 - (s43 * Γ2 * s43 * Γ1 + s33 * Γ1 + s44 * Γ2) + (s33 * Γ1 * s44 * Γ2)) = s11p eq2 = (s32*Γ1*s31*(1-(s44*Γ2)+0)+s32*Γ1*s43*Γ2*s41+s42*Γ2 *s41*(1-(s33*Γ1))+s42*Γ2*s43*Γ1*s31+s21*(1 -(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2))) / (1 -(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2)) = s21p eq3 = (s32*Γ1*s32*(1-(s44*Γ2)+0)+s32*Γ1*s43*Γ2*s42*(1-0+0)+s22*(1 -(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2))+s42*Γ2* s42*(1-(s33*Γ1)+0)+s42*Γ2*s43*Γ1*s32*(1-0+0)) / (1 -(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2)) = s22p 我试图用sympy的nsolve函数以及（稍作修改后）MATLAB的fsolve和lsqnonlin函数来解决这个问题，但是我对此并不太幸运。 nsolve出现了错误，但无法找到绑定结果的方法。另一方面，MATLAB几乎立即停止运行，并返回了与我为起点输入的任何猜测都非常接近的内容-虽然值在某种意义上是有界的，但它会尝试为我指定了实界的东西返回复数，所以我不确定这是怎么回事。
我在python中的尝试看起来像这样-我已经解决了这个问题，并包括了足够的数据集以能够运行脚本-混淆了读取和处理数据的位，并且这是事实将循环运行约1000次，对应的数据与其他频率下的测量结果略有不同。
import sympy
from sympy.core.power import Pow
from sympy import I as j
from sympy import Rel,Eq,Abs,nsolve
import sys
if __name__ == "__main__":
# these are real valued.
s11db,s21db,s22db,s31db,s32db,s33db,s41db,s42db,s43db,s44db=sympy.symbols('s11db s21db s22db s31db s32db s33db s41db s42db s43db s44db',real=True)
s11ph,s21ph,s22ph,s31ph,s32ph,s33ph,s41ph,s42ph,s43ph,s44ph=sympy.symbols('s11ph s21ph s22ph s31ph s32ph s33ph s41ph s42ph s43ph s44ph',real=True)
s11,s21,s22,s31,s32,s33,s41,s42,s43,s44=sympy.symbols('s11 s21 s22 s22 s31 s33 s41 s42 s43 s44')
variablesdb=[s11db,s21db,s22db,s31db,s32db,s33db,s41db,s42db,s43db,s44db]
variablesph=[s11ph,s21ph,s22ph,s31ph,s32ph,s33ph,s41ph,s42ph,s43ph,s44ph]
variables = []
variables.extend(variablesdb)
variables.extend(variablesph)
guessesdb=[-20,-20,-20,-20,-20,-20,-20,-20,-20,-20]
guessesph = [0,0,0,0,0,0,0,0,0,0]
guesses = []
guesses.extend(guessesdb)
guesses.extend(guessesph)
s11 = Pow(10,(s11db/10)) * sympy.exp(j*s11ph)
s21 = Pow(10,(s21db/10)) * sympy.exp(j*s21ph)
s22 = Pow(10,(s22db/10)) * sympy.exp(j*s22ph)
s31 = Pow(10,(s31db/10)) * sympy.exp(j*s31ph)
s32 = Pow(10,(s32db/10)) * sympy.exp(j*s32ph)
s33 = Pow(10,(s33db/10)) * sympy.exp(j*s33ph)
s41 = Pow(10,(s41db/10)) * sympy.exp(j*s41ph)
s42 = Pow(10,(s42db/10)) * sympy.exp(j*s42ph)
s43 = Pow(10,(s43db/10)) * sympy.exp(j*s43ph)
s44 = Pow(10,(s44db/10)) * sympy.exp(j*s44ph)
mΓ1=[0.016748068572860958+0.02165573563670993j,\
0.3524499197684044+1.01318272809286j, \
0.9902384965572285-0.4090088119125726j,\
0.9902384965572285-0.4090088119125726j,\
0.8639480592070746-0.6312186830903206j,\
0.8481899405540196-0.6521365553445038j,\
0.8639480592070746-0.6312186830903206j,\
0.8483474812536963-0.6520564750063542j,\
0.8599389505285947-0.6367147924208255j,\
0.844854651665744-0.6565142490941961j,\
0.8599389505285947-0.6367147924208255j,\
0.8596032944139899-0.6370905579072205j,\
0.12178304066817838+0.23419002409258938j,\
0.25810610017732+0.11233917791430391j,\
-0.7881852138612478+0.2941231164148775j,\
0.8537371705084078+0.46722896692551863j,\
0.14050135652671147+0.3073232683089746j,\
0.09456025715651947+0.3638487655929115j,\
0.7994228601750994+0.36132988231302926j,\
-0.5729577316366177+0.3829110562916974j]
mΓ2=[0.01886638946281557+0.02291153736938316j,\
0.41115632130362006+0.9904544135078267j,\
-1.0602110266293787+0.12873002023570793j,\
-1.0602110266293787+0.12873002023570793j,\
0.12178304066817838+0.23419002409258938j,\
0.25810610017732+0.11233917791430391j,\
-0.7881852138612478+0.2941231164148775j,\
0.8537371705084078+0.46722896692551863j,\
0.14050135652671147+0.3073232683089746j,\
0.09456025715651947+0.3638487655929115j,\
0.7994228601750994+0.36132988231302926j,\
-0.5729577316366177+0.3829110562916974j,\
0.8639480592070746-0.6312186830903206j,\
0.8481899405540196-0.6521365553445038j,\
0.8639480592070746-0.6312186830903206j,\
0.8483474812536963-0.6520564750063542j,\
0.8599389505285947-0.6367147924208255j,\
0.844854651665744-0.6565142490941961j,\
0.8599389505285947-0.6367147924208255j,\
0.8596032944139899-0.6370905579072205j]
ms11p=[-0.06420852176004949+0.007606136138496574j,\
-0.537896794882235+0.4041100722456898j,\
0.1412012055714567+0.5635279161255173j,\
0.1937727600367866+0.4292234886620929j,\
0.1469515332766462+0.5314862612846253j,\
0.1518709241016716+0.5310052159365275j,\
0.1578520689668907+0.5331598663019053j,\
0.1540581804228726+0.5392467784421259j,\
0.1431331385368337+0.5325073157781589j,\
0.1535928910517443+0.5342246999907136j,\
0.1512448255547076+0.5392322097605335j,\
0.1473241418663376+0.5318193110512432j,\
-0.2363809027693515+0.1174058767406709j,\
-0.163391416393593+0.08774117501580507j,\
-0.4568108580888767-0.2143100180166485j,\
0.193345944206058-0.04761628164099008j,\
-0.237571999125396+0.1195031741294373j,\
-0.2034191332391219+0.1681349627984851j,\
0.03011118465025885-0.09412680418431373j,\
-0.04464063283385059+0.3528230797332958j]
ms21p=[-0.003180197204485371-0.002823666341755482j,\
-0.1906367035405745+0.0686968862920421j,\
-0.05526246736964503+0.02635489796438511j,\
0.002518013584325512+0.06590835456839306j,\
-0.03537661863079149+0.08706407624479129j,\
-0.01805052294769753+0.08243849033759301j,\
-0.07056927627440178+0.03869620150023408j,\
0.02738173375006136+0.04029812368020189j,\
-0.02971165660146774+0.08491907637374546j,\
-0.02737424760484992+0.09187392180460466j,\
0.001038602543646932+0.03696165082008047j,\
0.01181224381379054+0.1271817903117702j,\
-0.03214857971468995+0.1284805218320462j,\
-0.01717410586879602+0.1297380448848506j,\
-0.03433798336733016+0.06369364773628092j,\
0.043654417400282+0.1396753864320528j,\
-0.03468050284302436+0.1289630391584528j,\
-0.03705274246077175+0.1366485542793229j,\
0.02030699854620357+0.1217902270543576j,\
-0.0236005339029794+0.1770862461678967j]
ms22p=[-0.1624942212974896-0.004693967380702952j,\
-0.284542316854743-0.7064738951728178j,\
0.4536543123936424-0.4578177740567013j,\
-0.1024338501849106-0.1979272945233151j,\
-0.1097995580875526-0.198389196010508j,\
-0.07902213390718082-0.08677717201029744j,\
0.3602007967821697-0.4162898752449462j,\
0.1822099768155973+0.3712680606767835j,\
-0.09809454238916153-0.1552338582896147j,\
-0.1688279742308047-0.115960920204985j,\
0.2331637616249181+0.1528931729845114j,\
-0.4800053258074745+0.09873053685139994j,\
-0.7738872200476685-0.04023055998635539j,\
-0.7721863083906342-0.02871292187380659j,\
-0.7821436668003945-0.03478684929877463j,\
-0.7693920578775125-0.04497757228074708j,\
-0.7737959107116188-0.03930039783428967j,\
-0.7721735572720234-0.03995110444341842j,\
-0.7729703027226912-0.04204217380573128j,\
-0.766856877704361-0.04045169338481738j]
equations = [] # sympy equations
# equations.append(Rel(s11db,0,'<='))
# equations.append(Rel(s21db,0,'<='))
# equations.append(Rel(s22db,0,'<='))
# equations.append(Rel(s31db,0,'<='))
# equations.append(Rel(s32db,0,'<='))
# equations.append(Rel(s33db,0,'<='))
# equations.append(Rel(s41db,0,'<='))
# equations.append(Rel(s42db,0,'<='))
# equations.append(Rel(s43db,0,'<='))
# equations.append(Rel(s44db,0,'<='))
# equations.append(Rel(s11db,-80,'>='))
# equations.append(Rel(s21db,-80,'>='))
# equations.append(Rel(s22db,-80,'>='))
# equations.append(Rel(s31db,-80,'>='))
# equations.append(Rel(s32db,-80,'>='))
# equations.append(Rel(s33db,-80,'>='))
# equations.append(Rel(s41db,-80,'>='))
# equations.append(Rel(s42db,-80,'>='))
# equations.append(Rel(s43db,-80,'>='))
# equations.append(Rel(s44db,-80,'>='))
# equations.append(Rel(s11ph,0,'>='))
# equations.append(Rel(s21ph,0,'>='))
# equations.append(Rel(s22ph,0,'>='))
# equations.append(Rel(s31ph,0,'>='))
# equations.append(Rel(s32ph,0,'>='))
# equations.append(Rel(s33ph,0,'>='))
# equations.append(Rel(s41ph,0,'>='))
# equations.append(Rel(s42ph,0,'>='))
# equations.append(Rel(s43ph,0,'>='))
# equations.append(Rel(s44ph,0,'>='))
# equations.append(Rel(s11ph,2*sympy.pi,'<='))
# equations.append(Rel(s21ph,2*sympy.pi,'<='))
# equations.append(Rel(s22ph,2*sympy.pi,'<='))
# equations.append(Rel(s31ph,2*sympy.pi,'<='))
# equations.append(Rel(s32ph,2*sympy.pi,'<='))
# equations.append(Rel(s33ph,2*sympy.pi,'<='))
# equations.append(Rel(s41ph,2*sympy.pi,'<='))
# equations.append(Rel(s42ph,2*sympy.pi,'<='))
# equations.append(Rel(s43ph,2*sympy.pi,'<='))
# equations.append(Rel(s44ph,2*sympy.pi,'<='))
for n in range(len(mΓ1)):
Γ1 = mΓ1[n]
Γ2 = mΓ2[n]
s11p = ms11p[n]
s21p = ms21p[n]
s22p = ms22p[n]
eq1 = Eq(((s11 * (1 - (s43 * Γ2 * s43 * Γ1 + s33 * Γ1 + s44 * Γ2) \
+ (s33 * Γ1 * s44 * Γ2)) + s31 * Γ1 * s31 * (1-(s44 * Γ2)) \
+ s31 * Γ1 * s43 * Γ2 * s41 + s41 * Γ2 * s41 * (1-( s33 * Γ1))\
+ s41 * Γ2 * s43 * Γ1 * s31) / (1 - (s43 * Γ2 * s43 * Γ1 \
+ s33 * Γ1 + s44 * Γ2) + (s33 * Γ1 * s44 * Γ2))),
s11p, evaluate=False)
eq2 = Eq((s32*Γ1*s31*(1-(s44*Γ2)+0)+s32*Γ1*s43*Γ2*s41+s42*Γ2 \
*s41*(1-(s33*Γ1))+s42*Γ2*s43*Γ1*s31+s21*(1 \
-(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2))) / (1 \
-(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2)),
s21p, evaluate=False)
eq3 = Eq((s32*Γ1*s32*(1-(s44*Γ2)+0)+s32*Γ1*s43*Γ2*s42*(1-0+0)+s22*(1 \
-(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2))+s42*Γ2* \
s42*(1-(s33*Γ1)+0)+s42*Γ2*s43*Γ1*s32*(1-0+0)) / (1 \
-(s43*Γ2*s43*Γ1+s33*Γ1+s44*Γ2)+(s33*Γ1*s44*Γ2)),
s22p, evaluate=False)
equations.append(eq1)
equations.append(eq2)
equations.append(eq3)
print(nsolve(equations,variables,guesses))
sys.exit(0)
# would like to add the following constraints:
# between one frequency and the next, dB value should be fairly stable
# probably not more than a few dB of variation, phase value should be stable
# too
# dB values should be constrained between -80dB and 0dB
# phase values should be constrained between 0 and 2π
# low loss passivity conditions:
# |s11|^2 + |s21|^2 + |s31|^2 + |s41|^2 <= 1 (should be close to 1 if low loss)
# |s21|^2 + |s22|^2 + |s32|^2 + |s42|^2 <= 1 (should be close to 1 if low loss)
# |s31|^2 + |s32|^2 + |s33|^2 + |s43|^2 <= 1 (should be close to 1 if low loss)
# |s41|^2 + |s42|^2 + |s43|^2 + |s44|^2 <= 1 (should be close to 1 if low loss)
# |sji|^2 <= 1 ;; equivalent to sjidb <= 0
%% fbnd.m
function F = fbnd(x)
mr1=[0.016748068572860958+0.02165573563670993j
0.3524499197684044+1.01318272809286j
0.9902384965572285-0.4090088119125726j
0.9902384965572285-0.4090088119125726j
0.8639480592070746-0.6312186830903206j
0.8481899405540196-0.6521365553445038j
0.8639480592070746-0.6312186830903206j
0.8483474812536963-0.6520564750063542j
0.8599389505285947-0.6367147924208255j
0.844854651665744-0.6565142490941961j
0.8599389505285947-0.6367147924208255j
0.8596032944139899-0.6370905579072205j
0.12178304066817838+0.23419002409258938j
0.25810610017732+0.11233917791430391j
-0.7881852138612478+0.2941231164148775j
0.8537371705084078+0.46722896692551863j
0.14050135652671147+0.3073232683089746j
0.09456025715651947+0.3638487655929115j
0.7994228601750994+0.36132988231302926j
-0.5729577316366177+0.3829110562916974j];
mr2=[0.01886638946281557+0.02291153736938316j
0.41115632130362006+0.9904544135078267j
-1.0602110266293787+0.12873002023570793j
-1.0602110266293787+0.12873002023570793j
0.12178304066817838+0.23419002409258938j
0.25810610017732+0.11233917791430391j
-0.7881852138612478+0.2941231164148775j
0.8537371705084078+0.46722896692551863j
0.14050135652671147+0.3073232683089746j
0.09456025715651947+0.3638487655929115j
0.7994228601750994+0.36132988231302926j
-0.5729577316366177+0.3829110562916974j
0.8639480592070746-0.6312186830903206j
0.8481899405540196-0.6521365553445038j
0.8639480592070746-0.6312186830903206j
0.8483474812536963-0.6520564750063542j
0.8599389505285947-0.6367147924208255j
0.844854651665744-0.6565142490941961j
0.8599389505285947-0.6367147924208255j
0.8596032944139899-0.6370905579072205j];
ms11p=[-0.06420852176004949+0.007606136138496574j
-0.537896794882235+0.4041100722456898j
0.1412012055714567+0.5635279161255173j
0.1937727600367866+0.4292234886620929j
0.1469515332766462+0.5314862612846253j
0.1518709241016716+0.5310052159365275j
0.1578520689668907+0.5331598663019053j
0.1540581804228726+0.5392467784421259j
0.1431331385368337+0.5325073157781589j
0.1535928910517443+0.5342246999907136j
0.1512448255547076+0.5392322097605335j
0.1473241418663376+0.5318193110512432j
-0.2363809027693515+0.1174058767406709j
-0.163391416393593+0.08774117501580507j
-0.4568108580888767-0.2143100180166485j
0.193345944206058-0.04761628164099008j
-0.237571999125396+0.1195031741294373j
-0.2034191332391219+0.1681349627984851j
0.03011118465025885-0.09412680418431373j
-0.04464063283385059+0.3528230797332958j];
ms21p=[-0.003180197204485371-0.002823666341755482j
-0.1906367035405745+0.0686968862920421j
-0.05526246736964503+0.02635489796438511j
0.002518013584325512+0.06590835456839306j
-0.03537661863079149+0.08706407624479129j
-0.01805052294769753+0.08243849033759301j
-0.07056927627440178+0.03869620150023408j
0.02738173375006136+0.04029812368020189j
-0.02971165660146774+0.08491907637374546j
-0.02737424760484992+0.09187392180460466j
0.001038602543646932+0.03696165082008047j
0.01181224381379054+0.1271817903117702j
-0.03214857971468995+0.1284805218320462j
-0.01717410586879602+0.1297380448848506j
-0.03433798336733016+0.06369364773628092j
0.043654417400282+0.1396753864320528j
-0.03468050284302436+0.1289630391584528j
-0.03705274246077175+0.1366485542793229j
0.02030699854620357+0.1217902270543576j
-0.0236005339029794+0.1770862461678967j];
ms22p=[-0.1624942212974896-0.004693967380702952j
-0.284542316854743-0.7064738951728178j
0.4536543123936424-0.4578177740567013j
-0.1024338501849106-0.1979272945233151j
-0.1097995580875526-0.198389196010508j
-0.07902213390718082-0.08677717201029744j
0.3602007967821697-0.4162898752449462j
0.1822099768155973+0.3712680606767835j
-0.09809454238916153-0.1552338582896147j
-0.1688279742308047-0.115960920204985j
0.2331637616249181+0.1528931729845114j
-0.4800053258074745+0.09873053685139994j
-0.7738872200476685-0.04023055998635539j
-0.7721863083906342-0.02871292187380659j
-0.7821436668003945-0.03478684929877463j
-0.7693920578775125-0.04497757228074708j
-0.7737959107116188-0.03930039783428967j
-0.7721735572720234-0.03995110444341842j
-0.7729703027226912-0.04204217380573128j
-0.766856877704361-0.04045169338481738j];
F = zeros(60);
for n=1:length(ms22p)
r1 = mr1(n);
r2 = mr2(n);
s11p = ms11p(n);
s21p = ms21p(n);
s22p = ms22p(n);
s11db = x(1);
s21db = x(2);
s22db = x(3);
s31db = x(4);
s32db = x(5);
s33db = x(6);
s41db = x(7);
s42db = x(8);
s43db = x(9);
s44db = x(10);
s11ph = x(11);
s21ph = x(12);
s22ph = x(13);
s31ph = x(14);
s32ph = x(15);
s33ph = x(16);
s41ph = x(17);
s42ph = x(18);
s43ph = x(19);
s44ph = x(20);
s = @(db,ph) 10.^(db/10) * exp(1j*ph);
index = 3*(n-1)+1;
F(index) = ((s(s11db,s11ph) * (1 - (s(s43db,s43ph) * r2 * s(s43db,s43ph) * r1 + s(s33db,s33ph) * r1 + s(s44db,s44ph) * r2) ...
+ (s(s33db,s33ph) * r1 * s(s44db,s44ph) * r2)) + s(s31db,s31ph) * r1 * s(s31db,s31ph) * (1-(s(s44db,s44ph) * r2)) ...
+ s(s31db,s31ph) * r1 * s(s43db,s43ph) * r2 * s(s41db,s41ph) + s(s41db,s41ph) * r2 * s(s41db,s41ph) * (1-( s(s33db,s33ph) * r1)) ...
+ s(s41db,s41ph) * r2 * s(s43db,s43ph) * r1 * s(s31db,s31ph)) / (1 - (s(s43db,s43ph) * r2 * s(s43db,s43ph) * r1 ...
+ s(s33db,s33ph) * r1 + s(s44db,s44ph) * r2) + (s(s33db,s33ph) * r1 * s(s44db,s44ph) * r2))) - s11p;
index = 3*(n-1)+2;
F(index) = ((s(s32db,s32ph)*r1*s(s31db,s31ph)*(1-(s(s44db,s44ph)*r2)+0)+s(s32db,s32ph)*r1*s(s43db,s43ph)*r2*s(s41db,s41ph)+s(s42db,s42ph)*r2 ...
*s(s41db,s41ph)*(1-(s(s33db,s33ph)*r1))+s(s42db,s42ph)*r2*s(s43db,s43ph)*r1*s(s31db,s31ph)+s(s21db,s21ph)*(1 ...
-(s(s43db,s43ph)*r2*s(s43db,s43ph)*r1+s(s33db,s33ph)*r1+s(s44db,s44ph)*r2)+(s(s33db,s33ph)*r1*s(s44db,s44ph)*r2))) / (1 ...
-(s(s43db,s43ph)*r2*s(s43db,s43ph)*r1+s(s33db,s33ph)*r1+s(s44db,s44ph)*r2)+(s(s33db,s33ph)*r1*s(s44db,s44ph)*r2))) - s21p;
index = 3*(n-1)+3;
F(index) = ((s(s32db,s32ph)*r1*s(s32db,s32ph)*(1-(s(s44db,s44ph)*r2)+0)+s(s32db,s32ph)*r1*s(s43db,s43ph)*r2*s(s42db,s42ph)*(1-0+0)+s(s22db,s22ph)*(1 ...
-(s(s43db,s43ph)*r2*s(s43db,s43ph)*r1+s(s33db,s33ph)*r1+s(s44db,s44ph)*r2)+(s(s33db,s33ph)*r1*s(s44db,s44ph)*r2))+s(s42db,s42ph)*r2* ...
s(s42db,s42ph)*(1-(s(s33db,s33ph)*r1)+0)+s(s42db,s42ph)*r2*s(s43db,s43ph)*r1*s(s32db,s32ph)*(1-0+0)) / (1 ...
-(s(s43db,s43ph)*r2*s(s43db,s43ph)*r1+s(s33db,s33ph)*r1+s(s44db,s44ph)*r2)+(s(s33db,s33ph)*r1*s(s44db,s44ph)*r2))) - s22p;
end
end
%% solve.m
lb = -80* [1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0];
ub = 2*pi* [0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1];
guesses = [-40,-40,-40,-40,-40,-40,-40,-40,-40,-40,0,0,0,0,0,0,0,0,0,0];
options = optimoptions(@lsqnonlin,'Algorithm','trust-region-reflective',...
'MaxIterations',5000,...
'Display','Iter-detailed'...
);
%$[x,res] = lsqnonlin(@fbnd,guesses,lb,ub,options);
optionsf = optimoptions(@fsolve,'Algorithm','trust-region',...
'Diagnostics','on')
[x,res] = fsolve(@fbnd,guesses)
我希望/希望在我指定的范围内得到一个解决方案（即合理的解决方案）。我不确定我使用的是正确的工具，还是不确定当前使用的工具是否正确。如果有人愿意向我指出正确的方向，我将不胜感激。