Let f be an arithmetic function and A={k1,k2,...,kn} are integers in increasing order.
Now I want to start with k1 and compare f(ki) with f(k1). If f(ki)>f(k1), put ki as k1.
Now start with ki, and compare f(kj) with f(ki), for j>i. If f(kj)>f(ki), put kj as ki, and repeat this procedure.
At the end we will have a sub sequence B={L1,...,Lm} of A by this property:
L1=k1
L2=ki
L3=kj
...
where
f(L(i+1))>f(L(i)), for any 1<=i<=m-1
For example, let f be the divisor function of integers.
I think there should be some way to do more efficient and faster than I did.
Do you know how to write a code for my purpose in Mathematica or Matlab.
Mathematica is preferable.
«««««««««««««««««««««««««««««««
I have written a code for this program with Mathematica, and it take some hours to compute f of ki's or the set B for large numbers.
Here I put some part of my code and this is just a sample and the question in my program could be more larger than these:
the space between g's are product. for example:
g[67757] g[353]=g[67757]*g[353]
««««««««««««««««««««««««««««««««««««
f[n_] := DivisorSigma[0, n];
g[n_] := Product[Prime[i], {i, 1, PrimePi[n]}];
k1 = g[67757] g[353] g[59] g[19] g[11] g[7] g[5]^2 6^3 2^7;
k2 = g[67757] g[353] g[59] g[19] g[11] g[7] g[5] 6^5 2^7;
k3 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5] 6^4 2^7;
k4 = g[67759] g[349] g[53] g[19] g[11] g[7] g[5] 6^5 2^6;
k5 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5] 6^4 2^8;
k6 = g[67759] g[349] g[53] g[19] g[11] g[7] g[5]^2 6^3 2^7;
k7 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5] 6^5 2^6;
k8 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5] 6^4 2^9;
k9 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5]^2 6^3 2^7;
k10 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5] 6^5 2^7;
k11 = g[67759] g[349] g[53] g[19] g[11] g[7] g[5]^2 6^4 2^6;
k12 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5]^2 6^3 2^8;
k13 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5]^2 6^4 2^6;
k14 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5]^2 6^3 2^9;
k15 = g[67757] g[359] g[53] g[19] g[11] g[7] g[5]^2 6^4 2^7;
k16 = g[67757] g[359] g[53] g[23] g[11] g[7] g[5] 6^4 2^8;
k17 = g[67757] g[359] g[59] g[19] g[11] g[7] g[5] 6^4 2^7;
k18 = g[67757] g[359] g[53] g[23] g[11] g[7] g[5] 6^4 2^9;
k19 = g[67759] g[353] g[53] g[19] g[11] g[7] g[5] 6^4 2^6;
k20 = g[67763] g[347] g[53] g[19] g[11] g[7] g[5] 6^4 2^7;
k = Table[k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, k12, k13, k14, k15, k16, k17, k18, k19, k20];
i = 1;
count = 0;
For[j = i, j <= 20, j++,
If[f[k[[j]]] - f[k[[i]]] > 0, i = j; Print["k",i];
count = count + 1]];
Print["count= ", count]
««««««««««««««««««««««««««««««««««««
DivisorSigma has to factor the numbers (it has no idea how they were constructed). You can speed this substantially by removing the gcd of the list. In detail:
Compute new list as old list / gcd.
Factor the gcd.
Use a function that, given a pair of integers in factored form, merges the factorization (so you have their product in factored form).
Then for any two elements in the reduced list, you compare by merging their factorizations each with that of the gcd, and invoking a function to compute the number of divisors when given the factored form. That last is simply the product of the exponents each increased by one.
In code:
kgcd = GCD ## k;
newk = k/kgcd;
gcdfacs = FactorInteger[kgcd];
sumDivisors[faclist_] := Times ## (1 + faclist[[All, 2]])
mergeFactorLists[fl1_, fl2_] :=
Flatten[GatherBy[Join[fl1, fl2], First] /.
{{p1_Integer,e1_Integer}, {p1_,e2_Integer}} -> {{p1,e1+e2}}, 1]
f2[v1_] := sumDivisors[mergeFactorLists[FactorInteger[v1], gcdfacs]]
Here is your example, with f2 applied to elements of newk.
Timing[i = 1;
count = 0;
For[j = i, j <= 20, j++,
If[f2[newk[[j]]] - f2[newk[[i]]] > 0, i = j; Print["k", i];
count = count + 1]];
Print["count= ", count]]
During evaluation of In[140]:= k2
During evaluation of In[140]:= k5
During evaluation of In[140]:= k7
During evaluation of In[140]:= k8
During evaluation of In[140]:= k9
During evaluation of In[140]:= k10
During evaluation of In[140]:= k12
During evaluation of In[140]:= k13
During evaluation of In[140]:= k14
During evaluation of In[140]:= k15
During evaluation of In[140]:= k16
During evaluation of In[140]:= k17
During evaluation of In[140]:= k18
During evaluation of In[140]:= count= 13
Out[140]= {0.539918, Null}
As others commented, you might instead want to do SortBy or perhaps
sortedk = k[[Ordering[newk, All, f2[#1] < f2[#2] &]]];
--update 2011-02-01--
Here are the various requested function, made to operate on integers represented as lists of their prime factors and corresponding powers. We use utility functions to "multiply" two or more such representations, so that they are easily constructed from the definition for g[] above.
logarithm[fl_] := fl[[All,2]] . Log[fl[[All,1]]]
divSigma[k_, fax_] := Times ##
((fax[[All, 1]]^(k*(fax[[All, 2]] + 1)) - 1)/(fax[[All, 1]]^k - 1))
mergeFactorLists[f1_,f2_,f3__] :=
mergeFactorLists[mergeFactorLists[f1,f2],f3]
mergeFactorLists[fl1_, fl2_] :=
Flatten[GatherBy[Join[fl1, fl2], First] /.
{{p1_Integer,e1_Integer}, {p1_,e2_Integer}} -> {{p1,e1+e2}}, 1]
eulerPhi[fl_] :=
Times ## ((fl[[All, 1]] - 1)*fl[[All, 1]]^(fl[[All, 2]] - 1))
I use factorlist in a manner similar to use of g[] above, but to obtain the factored lists rather than the integer itself. For ease of converting code, you might do as below.
g[n__] := factorList[n]
Then you would construct k1 et al as:
k1 = mergeFactorLists[g[67757], g[353], g[59], g[19], g[11], g[7],
g[5, 2], g[4, 3], g[2, 7]];
I remark that it might be better to use indexing e.g. k[1], k[2], etc. This way you can store the index instead of the number (whether represented as a factored list or fully expanded). This was a concern in either your comments or private email, I'm not sure.
Here is a short example to indicate that the functions might be working as advertised.
In[77]:= example =
mergeFactorLists[g[59], g[19], g[11], g[7], g[5, 2], g[4, 3], g[2, 7]]
Out[77]= {{2, 16}, {3, 9}, {5, 6}, {7, 4}, {11, 3}, {13, 2}, {17,
2}, {19, 2}, {23, 1}, {29, 1}, {31, 1}, {37, 1}, {41, 1}, {43,
1}, {47, 1}, {53, 1}, {59, 1}}
In[83]:= divSigma[2, example]
Out[83]= 8309625653259163198663074449058595410045270294408417958734031\
0136565010401600000000
In[92]:= eulerPhi[example]
Out[92]= 30117106786279162451552137484697600000000
In[95]:= examplenumber = Times ## Map[#[[1]]^#[[2]] &, example]
Out[95]= 225123336762006539948611826706656256000000
In[99]:= DivisorSigma[2, examplenumber]
Out[99]= 8309625653259163198663074449058595410045270294408417958734031\
0136565010401600000000
In[100]:= EulerPhi[examplenumber]
Out[100]= 30117106786279162451552137484697600000000
--end update--
Daniel Lichtblau
Wolfram Research
On my version of mathematica, most of the calculation time is spent applying the function f[n]. Even just f[k1] takes a few seconds.
In any case, what you want to do is use SortBy. This will take a list and a function as arguments. It applies the function to every member of the list and sorts them in order from least-to-greatest, so you'll need to swap the list around to greatest-to-least. Remember to use k = List[k1, k2, ... , k20] instead of k = Table[k1, k2, ... , k20] and you should be good.
Part of the reason why this is so slow is because you're using for's and if's in Mathematica. Neither are particularly fast.
Ordinarily it's recommended to try doing some list operation, as this is MUCH faster. I'm not sure how you could accomplish this off hand, but you may want to look into it.
Related
Below code is used to solve a stochastic equation numerically in Mathematica for one particle. I wonder if there is a way to generalize it to the case of more than one particle and average over them. Is there anyone who knows how to do that?
Clear["Global`*"]
{ a = Pi, , b = 2 Pi, l = 5, k = 1};
ic = x#tbegin == 1;
tbegin = 1;
tend = 400;
interval = {1, 25};
lst := NestWhileList[(# + RandomVariate#TruncatedDistribution[interval,
StableDistribution[1, 0.3, 0, 0, 1]]) &, tbegin, # < tend &];
F[t_] := Piecewise[{{k, Or ## #}}, -k] &[# <= t < #2 & ###
Partition[lst, 2]];
eqn := x'[t] == (F#(t)) ;
sol = NDSolveValue[{eqn, ic}, x, {t, tbegin, tend},
MaxSteps -> Infinity];
Plot[sol#t, {t, tbegin, tend}]
First[First[sol]]
Plot[sol'[t], {t, tbegin, tend}]
Plot[F[t], {t, tbegin, tend}]
You are my last hope.
In my university there are no people able to answer my question.
I've got a function quite complex depending on 6 paramethers a0,a1,a2,b0,b1,b2 that minimize the delta of pression, volume liquid and volume vapor calculated by a rather new equation of state.
NMinimize is very slow and I could not do any considerations about this equation because timing is very high.
In the code there are some explanations and some problems concerning my code.
On my knees I pray you to help me.
I'm sorry, but after 4 months on construction of these equation I could not test it. And frustration is increasing day after day!
Clear["Global`*"];
data = {{100., 34.376, 0.036554, 23.782}, {105., 56.377, 0.037143,
15.116}, {110., 88.13, 0.037768, 10.038}, {115., 132.21, 0.038431,
6.9171}, {120., 191.43, 0.039138, 4.9183}, {125., 268.76,
0.039896, 3.5915}, {130., 367.32, 0.040714, 2.6825}, {135.,
490.35, 0.0416, 2.0424}, {140., 641.18, 0.042569, 1.5803}, {145.,
823.22, 0.043636, 1.2393}, {150., 1040., 0.044825,
0.98256}, {155., 1295., 0.046165, 0.78568}, {160., 1592.1,
0.047702, 0.63206}, {165., 1935.1, 0.0495, 0.51014}, {170.,
2328.3, 0.051667, 0.41163}, {175., 2776.5, 0.054394,
0.33038}, {180., 3285.2, 0.058078, 0.26139}, {185., 3861.7,
0.063825, 0.19945}, {190., 4518.6, 0.079902, 0.12816}};
tvector = data[[All, 1]];(*K*)
pvector =
data[[All, 2]];(*KPa*)
vlvector = data[[All, 3]];(*L/mol*)
vvvector =
data[[All, 4]];
(*L/mol.*)
r = 8.314472;
tc = 190.56;
avvicinamento = Length[tvector] - 3;
trexp = Take[tvector, avvicinamento]/tc;
vlexp = Take[vlvector, avvicinamento];
vvexp = Take[vvvector, avvicinamento];
zeri = Table[i*0., {i, avvicinamento}];
pexp = Take[pvector, avvicinamento];
(*Function for calculation of Fugacity of CSD Equation*)
(*Function for calculation of Fugacity of CSD Equation*)
fug[v_, p_, t_, a_, b_] :=
Module[{y, z, vbv, vb, f1, f2, f3, f4, f}, y = b/(4 v);
z = (p v)/(r t);
vbv = Log[(v + b)/v];
vb = v + b;
f1 = (4*y - 3*y^2)/(1 - y)^2;
f2 = (4*y - 2*y^2)/(1 - y)^3;
f3 = (2*vbv)/(r t*b)*a;
f4 = (vbv/b - 1/vb)/(r t)*a;
f = f1 + f2 - f3 + f4 - Log[z];
Exp[f]]
(*g Minimize the equality of fugacity*)
g[p_?NumericQ, t_?NumericQ, a0_?NumericQ, a1_?NumericQ, a2_?NumericQ,
b0_?NumericQ, b1_?NumericQ, b2_?NumericQ] := Module[{},
a = a0*Exp[a1*t + a2*t^2];
b = b0 + b1*t + b2*t^2;
csd = a/(r*t*(b + v)) - (-(b^3/(64*v^3)) + b^2/(16*v^2) +
b/(4*v) + 1)/(1 - b/(4*v))^3 + (p*v)/(r*t);
vol = NSolve[csd == 0 && v > 0, v, Reals];
sol = v /. vol;
(*If[Length[sol]==1,Interrupt[];Print["Sol==1"]];*)
vliquid = Min[sol];
vvapor = Max[sol];
fl = fug[vliquid, p, t, a, b];
fv = fug[vvapor, p, t, a, b];
(*Print[{t,p,vol,Abs[fl-fv]}];*)
Abs[fl - fv]];
(*This function minimize the pcalc-pexp and vcalc-vexp *)
hope[a0_?NumericQ, a1_?NumericQ, a2_?NumericQ, b0_?NumericQ,
b1_?NumericQ, b2_?NumericQ] :=
Module[{},
pp[a0, a1, a2, b0, b1, b2] :=
Table[FindRoot[{g[p, tvector[[i]], a0, a1, a2, b0, b1, b2]},
{p,pvector[[i]]}],{i,avvicinamento}];
pressioni1 = pp[a0, a1, a2, b0, b1, b2];
pcalc = p /. pressioni1;
differenza = ((pcalc - pexp)/pexp)^2;
If[MemberQ[differenza, 0.],
differenza = zeri + RandomReal[{100000, 500000}];(*
First problem:
As I've FindRoot that finds the solutions equal to the starting \
point, I don't want these kind of solutions and with this method - \
+RandomReal[{100000,500000}] -
a keep away this solutions.Is it right? *)
deltap = Total[differenza],
differenzanonzero = Select[differenza, # > 0 &];
csd1[a_, b_, p_, t_] :=
a/(r*t*(b + v)) - (-(b^3/(64*v^3)) + b^2/(16*v^2) + b/(4*v) +
1)/(1 - b/(4*v))^3 + (p*v)/(r*t);(*Funzione CSD*)
volumi =
Table[NSolve[csd1[a, b, pcalc[[i]], tvector[[i]]], v, Reals], {i,
avvicinamento}];
soluzioni = v /. volumi;
vvcalc = Table[Max[soluzioni[[i]]], {i, avvicinamento}];
vlcalc = Table[Min[soluzioni[[i]]], {i, avvicinamento}];
deltavl = Total[((vlexp - vlcalc)/vlcalc)^2];
deltavv = Total[((vvexp - vvcalc)/vvcalc)^2];
deltap = Total[differenza];
Print[a0, " ", b0, " ", delta];
delta = 0.1*deltavl + 0.1*deltavv + deltap]];
NMinimize[{hope[a0, a1, a2, b0, b1, b2],
500 < a0 < 700 && -0.01 < a1 < -1.0*10^-5 && -10^-5 < a2 < -10^-7 &&
0.0010 < b0 < 0.1 && -0.0010 < b1 < -1.0*10^-5 &&
10^-9 < b2 < 10^-7}, {a0, a1, a2, b0, b1, b2}]
Thanks in advance!
Mariano Pierantozzi
PhD Student in chemical Engineering
Well, I'm having trouble with this code, it's about writing the Selection Sort alghorithm in Mathematica, but inverted, I mean, instead of searching for the smallest number and place it in the first position of a list, I need to search for the biggest one and place it in the last position.
I've written this code but as I'm new to Mathematica, I can't find the solution. It doesn't sort the list. Thank you very much for reading, your answers will be helpfull!
L = {};
n = Input["Input the size of the list (a number): "];
For[i = 1, i <= n, m = Input["Input a number to place in the list:"];
L = Append[L, m]; i++]
SelectSort[L] :=
Module[{n = 1, temp, xi = L, j}, While[n <= Length#L, temp = xi[[n]];
For[j = n, j <= Length#L, j++, If[xi[[j]] < temp, temp = xi[[j]]];];
xi[[n ;;]] = {temp}~Join~
Delete[xi[[n ;;]], First#Position[xi[[n ;;]], temp]];
n++;];
xi]
Print[L]
Here is a working version. In the SelectSort[] function I only had to change the function variable to a pattern variable, i.e. L_. Other than that it seems to work.
(* Function definition *)
SelectSort[L_] := Module[{n = 1, temp, xi = L, j},
While[n <= Length#L,
temp = xi[[n]];
For[j = n, j <= Length#L, j++,
If[xi[[j]] < temp, temp = xi[[j]]];
];
xi[[n ;;]] = {temp}~Join~
Delete[xi[[n ;;]], First#Position[xi[[n ;;]], temp]];
n++;];
xi]
(* Run section *)
L = {};
n = Input["Input the size of the list (a number): "];
For[i = 1, i <= n, m = Input["Input a number to place in the list:"];
L = Append[L, m]; i++]
SelectSort[L]
Print[L]
{3, 3, 5, 7, 8}
{8, 3, 5, 7, 3}
The output is first the sorted list from SelectSort[L], then the original input list,L.
I'm trying to calculate the fourier transform of a gaussian beam. Later I want to ad some modifications to the following example code. With the required stepsize of 1e-6 the calculation with 8 kernel takes 1244s on my workstation. The most consuming part is obviously the generation of uaperture. Has anyone ideas to improve the performance? Why does mathematica not create a packed list from my expression, when I'm having both real and complex values in it?
uin[gx_, gy_, z_] := Module[{w0 = L1[[1]], z0 = L1[[3]], w, R, \[Zeta], k},
w = w0 Sqrt[1 + (z/z0)^2];
R = z (1 + (z0/z)^2);
\[Zeta] = ArcTan[z/z0];
k = 2*Pi/193*^-9;
Developer`ToPackedArray[
ParallelTable[
w0/w Exp[-(x^2 + y^2)/w^2] Exp[-I k/2/R (x^2 + y^2)/2] Exp[-I k z*0 +
I \[Zeta]*0], {x, gx}, {y, gy}]
]
]
AbsoluteTiming[
dx = 1*^-6;
gx = Range[-8*^-3, 8*^-3, dx];
gy = gx;
d = 15*^-3;
uaperture = uin[gx, gy, d];
ufft = dx*dx* Fourier[uaperture];
uout = RotateRight[
Abs[ufft]*dx^2, {Floor[Length[gx]/2], Floor[Length[gx]/2]}];
]
Thanks in advance,
Johannes
You can speed it up by first vectorizing it (uin2), then compiling it (uin3):
In[1]:= L1 = {0.1, 0.2, 0.3};
In[2]:= uin[gx_, gy_, z_] :=
Module[{w0 = L1[[1]], z0 = L1[[3]], w, R, \[Zeta], k},
w = w0 Sqrt[1 + (z/z0)^2];
R = z (1 + (z0/z)^2);
\[Zeta] = ArcTan[z/z0];
k = 2*Pi/193*^-9;
ParallelTable[
w0/w Exp[-(x^2 + y^2)/
w^2] Exp[-I k/2/R (x^2 + y^2)/2] Exp[-I k z*0 +
I \[Zeta]*0], {x, gx}, {y, gy}]
]
In[3]:= uin2[gx_, gy_, z_] :=
Module[{w0 = L1[[1]], z0 = L1[[3]], w, R, \[Zeta], k, x, y},
w = w0 Sqrt[1 + (z/z0)^2];
R = z (1 + (z0/z)^2);
\[Zeta] = ArcTan[z/z0];
k = 2*Pi/193*^-9;
{x, y} = Transpose[Outer[List, gx, gy], {3, 2, 1}];
w0/w Exp[-(x^2 + y^2)/
w^2] Exp[-I k/2/R (x^2 + y^2)/2] Exp[-I k z*0 + I \[Zeta]*0]
]
In[4]:= uin3 =
Compile[{{gx, _Real, 1}, {gy, _Real, 1}, z},
Module[{w0 = L1[[1]], z0 = L1[[3]], w, R, \[Zeta], k, x, y},
w = w0 Sqrt[1 + (z/z0)^2];
R = z (1 + (z0/z)^2);
\[Zeta] = ArcTan[z/z0];
k = 2*Pi/193*^-9;
{x, y} = Transpose[Outer[List, gx, gy], {3, 2, 1}];
w0/w Exp[-(x^2 + y^2)/
w^2] Exp[-I k/2/R (x^2 + y^2)/2] Exp[-I k z*0 + I \[Zeta]*0]
],
CompilationOptions -> {"InlineExternalDefinitions" -> True}
];
In[5]:= dx = 1*^-5;
gx = Range[-8*^-3, 8*^-3, dx];
gy = gx;
d = 15*^-3;
In[9]:= r1 = uin[gx, gy, d]; // AbsoluteTiming
Out[9]= {67.9448862, Null}
In[10]:= r2 = uin2[gx, gy, d]; // AbsoluteTiming
Out[10]= {28.3326206, Null}
In[11]:= r3 = uin3[gx, gy, d]; // AbsoluteTiming
Out[11]= {0.4190239, Null}
We got a ~160x speedup even though this is not running in parallel.
Results are the same:
In[12]:= r1 == r2
Out[12]= True
There's a tiny difference here due to numerical errors:
In[13]:= r2 == r3
Out[13]= False
In[14]:= Max#Abs[r2 - r3]
Out[14]= 5.63627*10^-14
When I was trying to find the maximum value of f using NMaximize, mathematica gave me a error saying
NMaximize::cvdiv: Failed to converge to a solution. The function may be unbounded.
However, if I scale f with a large number, say, 10^5, 10^10, even 10^100, NMaximize works well.
In the two images below, the blue one is f, and the red one is f/10^10.
Here come my questions:
Is scaling a general optimization trick?
Any other robust, general workarounds for the optimizations such
needle-shape functions?
Because the scaling barely changed the shape of the needle-shape of
f, as shown in the two images, how can scaling work here?
thanks :)
Update1: with f included
Clear["Global`*"]
d = 1/100;
mu0 = 4 Pi 10^-7;
kN = 97/100;
r = 0.0005;
Rr = 0.02;
eta = 1.3;
e = 3*10^8;
s0 = 3/100;
smax = 1/100; ks = smax/s0;
fre = 1; tend = 1; T = 1;
s = s0*ks*Sin[2*Pi*fre*t];
u = D[s, t];
umax = N#First[Maximize[u, t]];
(*i=1;xh=0.1;xRp=4.5`;xLc=8.071428571428573`;
i=1;xh=0.1;xRp=4.5;xLc=8.714285714285715;*)
i = 1; xh = 0.1; xRp = 5.5; xLc = 3.571428571428571`;
(*i=1;xh=0.1`;xRp=5.`;xLc=6.785714285714287`;*)
h = xh/100; Rp = xRp/100; Lc = xLc/100;
Afai = Pi ((Rp + h + d)^2 - (Rp + h)^2);
(*Pi (Rp-Hc)^2== Afai*)
Hc = Rp - Sqrt[Afai/Pi];
(*2Pi(Rp+h/2) L/2==Afai*)
L = (2 Afai)/(\[Pi] (h + 2 Rp));
B = (n mu0 i)/(2 h);
(*tx = -3632B+2065934/10 B^2-1784442/10 B^3+50233/10 B^4+230234/10 \
B^5;*)
tx = 54830.3266978739 (1 - E^(-3.14250266080741 B^2.03187556833859));
n = Floor[(kN Lc Hc)/(Pi r^2)] ;
A = Pi*(Rp^2 - Rr^2);
b = 2*Pi*(Rp + h/2);
(* -------------------------------------------------------- *)
Dp0 = 2*tx/h*L;
Q0 = 0;
Q1 = ((1 - 3 (L tx)/(Dp h) + 4 (L^3 tx^3)/(Dp^3 h^3)) Dp h^3)/(
12 eta L) b;
Q = Piecewise[{{Q1, Dp > Dp0}, {Q0, True}}];
Dp = Abs[dp[t]];
ode = u A - A/e ((s0^2 - s^2)/(2 s0 )) dp'[t] == Q*Sign[dp[t]];
sol = First[
NDSolve[{ode, dp[0] == 0}, dp, {t, 0, tend} ,
MaxSteps -> 10^4(*Infinity*), MaxStepFraction -> 1/30]];
Plot[dp''[t] A /. sol, {t, T/4, 3 T/4}, AspectRatio -> 1,
PlotRange -> All]
Plot[dp''[t] A /10^10 /. sol, {t, T/4, 3 T/4}, AspectRatio -> 1,
PlotRange -> All, PlotStyle -> Red]
f = dp''[t] A /. sol;
NMaximize[{f, T/4 <= t <= 3 T/4}, t]
NMaximize[{f/10^5, T/4 <= t <= 3 T/4}, t]
NMaximize[{f/10^5, T/4 <= t <= 3 T/4}, t]
NMaximize[{f/10^10, T/4 <= t <= 3 T/4}, t]
update2: Here comes my real purpose. Actually, I am trying to make the following 3D region plot. But I found it is very time consuming (more than 3 hours), any ideas to speed up this region plot?
Clear["Global`*"]
d = 1/100;
mu0 = 4 Pi 10^-7;
kN = 97/100;
r = 0.0005;
Rr = 0.02;
eta = 1.3;
e = 3*10^8;
s0 = 3/100;
smax = 1/100; ks = smax/s0;
f = 1; tend = 1/f; T = 1/f;
s = s0*ks*Sin[2*Pi*f*t];
u = D[s, t];
umax = N#First[Maximize[u, t]];
du[i_?NumericQ, xh_?NumericQ, xRp_?NumericQ, xLc_?NumericQ] :=
Module[{Afai, Hc, L, B, tx, n, A, b, Dp0, Q0, Q1, Q, Dp, ode, sol,
sF, uF, width, h, Rp, Lc},
h = xh/100; Rp = xRp/100; Lc = xLc/100;
Afai = Pi ((Rp + h + d)^2 - (Rp + h)^2);
Hc = Rp - Sqrt[Afai/Pi];
L = (2 Afai)/(\[Pi] (h + 2 Rp));
B = (n mu0 i)/(2 h);
tx = 54830.3266978739 (1 - E^(-3.14250266080741 B^2.03187556833859));
n = Floor[(kN Lc Hc)/(Pi r^2)] ;
A = Pi*(Rp^2 - Rr^2);
b = 2*Pi*(Rp + h/2);
Dp0 = 2*tx/h*L;
Q0 = 0;
Q1 = ((1 - 3 (L tx)/(Dp h) + 4 (L^3 tx^3)/(Dp^3 h^3)) Dp h^3)/(
12 eta L) b;
Q = Piecewise[{{Q1, Dp > Dp0}, {Q0, True}}];
Dp = Abs[dp[t]];
ode = u A - A/e ((s0^2 - s^2)/(2 s0 )) dp'[t] == Q*Sign[dp[t]];
sol = First[
NDSolve[{ode, dp[0] == 0}, dp, {t, 0, tend} , MaxSteps -> 10^4,
MaxStepFraction -> 1/30]];
sF = ParametricPlot[{s, dp[t] A /. sol}, {t, 0, tend},
AspectRatio -> 1];
uF = ParametricPlot[{u, dp[t] A /. sol}, {t, 0, tend},
AspectRatio -> 1];
tdu = NMaximize[{dp''[t] A /10^8 /. sol, T/4 <= t <= 3 T/4}, {t,
T/4, 3 T/4}, AccuracyGoal -> 6, PrecisionGoal -> 6];
width = Abs[u /. tdu[[2]]];
{uF, width, B}]
RegionPlot3D[
du[1, h, Rp, Lc][[2]] <= umax/6, {h, 0.1, 0.2}, {Rp, 3, 10}, {Lc, 1,
10}, LabelStyle -> Directive[18]]
NMaximize::cvdiv is issued if the optimum improved a couple of orders of magnitude during the optimization process, and the final result is "large" in an absolute sense. (To prevent the message in a case where we go from 10^-6 to 1, for example.)
So yes, scaling the objective function can have an effect on this.
Strictly speaking this message is a warning, and not an error. My experience is that if you see it, there's a good chance that your problem is unbounded for some reason. In any case, this warning is a hint that you might want to double check your system to see if that might be the case.