When I write the expression for the partial derivative of a function, diff(f(x_1,x_2),x_1,1), for a function f created with funmake(f,[x_1,x_2]), the returned output is
However, when copying and pasting the output of the partial derivative, what I get instead is 'diff(f(x_1,x_2)), which stands for the total derivative of the function f instead of the partial derivative:
Since total and partial derivatives are not the same thing, this is inappropriate. What is the reason for this behavior? How could it be fixed?
As Robert Dodier wrote in the comments, this is a bug in wxMaxima. It is caused by the code that handles subscripts. (In your case, _1 and _2). Subscript cells don't implement a function that is supposed to serialize the variable of differentiation.
To work around the issue, you can avoid using subscripts. Changing x_1 and x_2 to x1 and x2 works:
(%i1) diff(f(x1,x2),x1,1); /* OK */
(%i2) diff(f(x_1,x_2),x_1,1); /* NOT OK */
Related
I have an expression that consists of functions of x and y, something like
ay+yf(x)+g(x)+bh(x)+k(y).
Is there a convenient method that removes all x-dependent terms and leaves ay+k(y)?
f,g,h,k are symbolic and not known functions.
As far as I know, dependence in maxima [defined with depends()] is only recognized in diff. I tried diff and then integrate/antidiff, but antidiff/integrate does not recognize y and b as constant, and gives an expression with integrals.
The following fragment produces a compilation error:
arma::Mat<double> a(10,10,arma::fill::zeros);
arma::ucolvec w = whatever1;
whatever2 = a.rows(w).each_col() + another-col-vector;
The error is that arma::subview_elem2 has no member named each_col.
In a number of cases in Armadillo, the standard array functions are not always available on expressions or results of other function calls. Clearly the rows() function does not return a Mat object, but a subview_elem2 object, presumably for optimization. Another way to do this would be to declare all the array functions in interfaces/pure abstract classes that Mat and other internal classes, such as subviews, implement. It seems it should be possible to make all Armadillo array expressions interchangeable with array objects aside from write operations for expressions that only generate r-values.
So... I could wish for the following
a) An explanation of which methods are not available for which results.
b) Preferably, enabling all combinations of array methods that make sense.
Absent the above, how can accomplish the desired result, which is to evaluate the expression:
a.rows(w).each_col()
??
Some prior information about armadillo
The armadillo library uses templates heavily and most operations return expression templates. Only when you assign the result to a variable the actual computation is performed. This is why you should not store the result of some computation with armadillo using auto.
For instance, given some matrices A, B and C, something like
auto D = A * B + C;
will not perform the computation and only the expression template is stored in D. On the other hand, using
arma::mat D = A * B + C;
will force the computation to happen and the result is stored in D.
Solution to your problem
Particularly to your question, something like a.rows(w) returns an expression template of type subview_elem2 (this file is defined in the source code armadillo_bits/subview_elem2_bones.hpp). This "temporary type" does not have a .each_col method, which results in the error you got. One way around is to store the result of a.rows(w) in a variable, but since you are not interested in the variable you can use the .eval() method. The .eval() method forces the expression template to perform the actual computation up to that point and thus the subsequent call to .each_col will work. That is, replace
a.rows(w).each_col() + another-col-vector;
with
a.rows(w).eval().each_col() + another-col-vector;
I wonder if there is any way to make functions defined within the main function be local, in a similar way to local variables. For example, in this function that calculates the gradient of a scalar function,
grad(var,f) := block([aux],
aux : [gradient, DfDx[i]],
gradient : [],
DfDx[i] := diff(f(x_1,x_2,x_3),var[i],1),
for i in [1,2,3] do (
gradient : append(gradient, [DfDx[i]])
),
return(gradient)
)$
The variable gradient that has been defined inside the main function grad(var,f) has no effect outside the main function, as it is inside the aux list. However, I have observed that the function DfDx, despite being inside the aux list, does have an effect outside the main function.
Is there any way to make the sub-functions defined inside the main function to be local only, in a similar way to what can be made with local variables? (I know that one can kill them once they have been used, but perhaps there is a more elegant way)
To address the problem you are needing to solve here, another way to compute the gradient is to say
grad(var, e) := makelist(diff(e, var1), var1, var);
and then you can say for example
grad([x, y, z], sin(x)*y/z);
to get
cos(x) y sin(x) sin(x) y
[--------, ------, - --------]
z z 2
z
(There isn't a built-in gradient function; this is an oversight.)
About local functions, bear in mind that all function definitions are global. However you can approximate a local function definition via local, which saves and restores all properties of a symbol. Since the function definition is a property, local has the effect of temporarily wiping out an existing function definition and later restoring it. In between you can create a temporary function definition. E.g.
foo(x) := 2*x;
bar(y) := block(local(foo), foo(x) := x - 1, foo(y));
bar(100); /* output is 99 */
foo(100); /* output is 200 */
However, I don't this you need to use local -- just makelist plus diff is enough to compute the gradient.
There is more to say about Maxima's scope rules, named and unnamed functions, etc. I'll try to come back to this question tomorrow.
To compute the gradient, my advice is to call makelist and diff as shown in my first answer. Let me take this opportunity to address some related topics.
I'll paste the definition of grad shown in the problem statement and use that to make some comments.
grad(var,f) := block([aux],
aux : [gradient, DfDx[i]],
gradient : [],
DfDx[i] := diff(f(x_1,x_2,x_3),var[i],1),
for i in [1,2,3] do (
gradient : append(gradient, [DfDx[i]])
),
return(gradient)
)$
(1) Maxima works mostly with expressions as opposed to functions. That's not causing a problem here, I just want to make it clear. E.g. in general one has to say diff(f(x), x) when f is a function, instead of diff(f, x), likewise integrate(f(x), ...) instead of integrate(f, ...).
(2) When gradient and Dfdx are to be the local variables, you have to name them in the list of variables for block. E.g. block([gradient, Dfdx], ...) -- Maxima won't understand block([aux], aux: ...).
(3) Note that a function defined with square brackets instead of parentheses, e.g. f[x] := ... instead of f(x) := ..., is a so-called array function in Maxima. An array function is a memoizing function, i.e. if f[x] is called two or more times, the return value is only computed once, and then returned every time thereafter. Sometimes that's a useful optimization when the domain of the function comprises a finite set.
(4) Bear in mind that x_1, x_2, x_3, are distinct symbols, not related to each other, and not related to x[1], x[2], x[3], even if they are displayed the same. My advice is to work with subscripted symbols x[i] when i is a variable.
(5) About building up return values, try to arrange to compute the whole thing at one go, instead of growing the result incrementally. In this case, makelist is preferable to for plus append.
(6) The return function in Maxima acts differently than in other programming languages; it's a little hard to explain. A function returns the value of the last expression which was evaluated, so if gradient is that last expression, you can just write grad(var, f) := block(..., gradient).
Hope this helps, I know it's obscure and complex. The Maxima programming language was not designed before being implemented, and some of the decisions are clearly questionable at the long interval of more than 50 years (!) later. That's okay, they were figuring it out as they went along. There was not a body of established results which could provide a point of reference; the original authors were contributing to what's considered common knowledge today.
I have a fairly large expression that involves a lot of subexpressions of the form (100*A^3 + 200*A^2 + 100*A)*x or (-A^2 - A)*y or (100*A^2 + 100*A)*z
I know, but I don't know how to tell Maxima this, that it in this case is valid to make the approximation A+1 ~ A, thereby effectively removing anything but the highest power of A in each coefficient.
I'm now looking for functions, tools, or methods that I can use to guide Maxima in dropping various terms that aren't important.
I have attempted with subst, but that requires me to specify each and every factor separately, because:
subst([A+1=B], (A+2)*(A+1)*2);
subst([A+1=B], (A+2)*(A*2+2));
(%o1) 2*(A+2)*B
(%o2) (A+2)*(2*A+2)
(that is, I need to add one expression for each slightly different variant)
I tried with ratsimp, but that's too eager to change every occurrence:
ratsubst(B, A+1, A*(A+1)*2);
ratsubst(B, A+1, A*(A*2+2));
(%o3) 2*B^2-2*B
(%o4) 2*B^2-2*B
which isn't actually simpler, as I would have preferred the answer to have been given as 2*B^2.
In another answer, (https://stackoverflow.com/a/22695050/5999883) the functions let and letsimp were suggested for the task of substituting values, but I fail to get them to really do anything:
x:(A+1)*A;
let ( A+1, B );
letsimp(x);
(x)A*(A+1)
(%o6) A+1 --\> B
(%o7) A^2+A
Again, I'd like to approximate this expression to A^2 (B^2, whatever it's called).
I understand that this is, in general, a hard problem (is e.g. A^2 + 10^8*A still okay to approximate as A^2?) but I think that what I'm looking for is a function or method of calculation that would be a little bit smarter than subst and can recognize that the same substitution could be done in the expression A^2+A as in the expression 100*A^2+100*A or -A^2-A instead of making me create a list of three (or twenty) individual substitutions when calling subst. The "nice" part of the full expression that I'm working on is that each of these A factors are of the form k*A^n*(A+1)^m for various small integers n, m, so I never actually end up with the degenerate case mentioned above.
(I was briefly thinking of re-expressing my expression as a polynomial in A, but this will not work as the only valid approximation of the expression (A^3+A^2+A)*x + y is A^3*x + y -- I know nothing about the relative sizes of x and y.
I'm trying to iterate over 2 parameters to get two splines for each pair. The code:
y_arr:[0.2487,0.40323333333333,0.55776666666667,0.7123]$
str_h_arr:[-0.8,-1.0,-1.2,-1.4]$
z_points:[0,0.1225,0.245,0.3675,0.49,0.6125,0.735,0.8575,0.98,1.1025,1.225,1.3475,1.47,
1.5925,1.715,1.8375,1.96,2.0825,2.205,2.26625,2.3275,2.3765,2.401,2.4255,2.43775,
2.4451,2.448775,2.45]$
length(a)$
length(b)$
load(interpol)$
for y_k:1 thru length(a) do (
for h_k:1 thru length(b) do (
y:y_arr[y_k],
str_h:str_h_arr[h_k],
bot_startpoints: [[-2.45,0],[0,y],[2.45,0]],
top_startpoints: [[-2.45,str_h_min],[0,y+str_h],[2.45,str_h_min]],
spline: cspline(bot_startpoints),
bot(x):=''spline,
print(bot(0))
)
);
//Part with top spline is skipped.
For all iterations output is now the same: 0.7123
What I want to get is two splines like in picture
Members of y_arr are y values in x=0, str_h_arr: height between splines in x=0.
So bot(0) should give me all values from y_arr.
If i don't use loop and just give this block values of y_k and h_k, it's working properly.
Can anybody point me to where I'm (or Maxima is) wrong with using loop with cspline?
The problem is that quote-quote (two single quotes, '') is applied only once, when it is read in input; it is not applied every time the expression in evaluated in the loop.
Looks like you need only to evaluate the spline at x = 0 and nothing else. So I'll suggest ev(spline, x=0) to evaluate it. You can also construct a lambda expression and evaluate that.
Here is the program after I've revised it as described above. Also, it is simpler and clearer to write for y in y_arr do (...) rather than making use of an explicit index for y_arr.
y_arr:[0.2487,0.40323333333333,0.55776666666667,0.7123]$
str_h_arr:[-0.8,-1.0,-1.2,-1.4]$
z_points:[0,0.1225,0.245,0.3675,0.49,0.6125,0.735,0.8575,0.98,1.1025,1.225,1.3475,1.47,
1.5925,1.715,1.8375,1.96,2.0825,2.205,2.26625,2.3275,2.3765,2.401,2.4255,2.43775,
2.4451,2.448775,2.45]$
load(interpol)$
for y in y_arr do (
for str_h in str_h_arr do (
bot_startpoints: [[-2.45,0],[0,y],[2.45,0]],
top_startpoints: [[-2.45,str_h_min],[0,y+str_h],[2.45,str_h_min]],
spline: cspline(bot_startpoints),
print (ev (spline, x=0))));
This is the output I get:
0.2487
0.2487
0.2487
0.2487
0.40323333333333
0.40323333333333
0.40323333333333
0.40323333333333
0.55776666666667
0.55776666666667
0.55776666666667
0.55776666666667
0.7123
0.7123
0.7123
0.7123