I'm puzzled by what I think is a mistake in a partial derivative I'm having Mathematica do for me.
Specifically, this is what I have:
Derivative I'd like to take
I'm trying to take the partial derivative of the following w.r.t. the variable θ (apologies for the formatting):
f=(1/4)(-4e((1+θ)/2)ψ+eN((1+θ)/2)ψ+eN((1+θ)/2-θd)ψ)-s
But the solution Mathematica produces seems very different from the one I get when I take the derivative myself. While Mathematica says the partial derivative of f w.r.t. θ is:
(1/4)eψ(N-2)
By hand, I get and am quite confident the correct answer is instead:
(1/4)eψ(N(1-d)-2)
That is, Mathematica is producing something that drops the variable d when it is differentiating. I've explored different functions that take a derivative in Mathematica, and the possibility that maybe some of the variables I'm using (such as d) might be protected or otherwise special, but I can't say that I know why the answer's so off. This is the first time in the notebook that d appears, so it is not set to 0. For context, I'm trying to confirm that the derivative of the function is positive for values of the variables in certain ranges, and we have d>0 and d<(1/2). Doing this all by hand works but I'm trying to confirm with Mathematica as I will be dealing with more complicated functions and need to make sure I'm having Mathematica produce the right derivatives.
Your didn't add spaces in eN and θd, so it thinks they're some other 2-character variables.
Adding spaces between them gives your expected result:
f[θ,e,N,ψ,d,s] = (1/4) (-4 e ((1+θ)/2) ψ + e N ((1+θ)/2) ψ + e N ((1+θ)/2 - θ d) ψ) - s;
D[f[θ, e, N, ψ, d, s], θ] // FullSimplify
(* 1/4 e (-2 + N - d N) ψ *)
Related
I would like to solve problems combining boolean and integer logic in linear arithmetic with a SAT/SMT solver. At first glance, Z3 seems promising.
First of all, is it at all possible to solve the following problem? This answer makes it seem like it works.
int x,y,z
boolean a,b,c
( (3x + y - 2z >= 10) OR (A AND (NOT B OR C)) OR ((A == C) AND (x + y >= 5)) )
If so, how does Z3 solve this kind of problem in theory and is there any documentation about it?
I could think of two ways to solve this problem. One would be to convert the Boolean operations into a linear integer expression. Another solution I read about is to use the Nelson-Oppen Combination Method described in [Kro 08].
I found a corresponding documentation in chapter 3.2.2. Solving Arithmetical Fragments, Table 1 a listing of the implemented algorithms for a certain logic.
Yes, SMT solvers are quite good at solving problems of this sort. Your problem can be expressed using z3's Python interface like this:
from z3 import *
x, y, z = Ints('x y z')
A, B, C = Bools('A B C')
solve (Or(3*x + y - 2*z >= 10
, And(A, Or(Not(B), C))
, And(A == C, x + y >= 5)))
This prints:
[A = True, z = 3, y = 0, B = True, C = True, x = 5]
giving you a (not necessarily "the") model that satisfies your constraints.
SMT solvers can deal with integers, machine words (i.e., bit-vectors), reals, along with many other data types, and there are efficient procedures for combinations of linear-integer-arithmetic, booleans, uninterpreted-functions, bit-vectors amongst many others.
See http://smtlib.cs.uiowa.edu for many resources on SMT solving, including references to other work. Any given solver (i.e., z3, yices, cvc etc.) will be a collection of various algorithms, heuristics and tactics. It's hard to compare them directly as each shine in their own way for certain sublogics, but for the base set of linear-integer arithmetic, booleans, and bit-vectors, they should all perform fairly well. Looks like you already found some good references, so you can do further reading as necessary; though for most end users it's neither necessary nor that important to know how an SMT solver internally works.
I am am mathmatica notebook to find an analytical solution to the follow constrained optimization problem:
Max y^(1-b)(x^b(1-a(x/(x+1)))) s.t. M = Px+qy
x,y
I have tried the following code:
Maximize[{y^(1-b)(x^b(1-a(x/(x+1)))), M==Px+qy}, {x,y}]
and in returns the same function as an output. In the function a, b, M, P, and q are all parameters. I have also tried assigning the parameters arbitrary values to test to see if mathmatica is not sure how to deal with the parameters. I used to following code:
Maximize[{y^(1-0.5)(x^0.5(1-0.75(x/(x+1)))), 1000=5x+5y},{x,y}]
and it returns the same function. However, if I remove the constraint it will solve the optimization problem.
Maximize[{y^(1-0.5)(x^0.5(1-0.75(x/(x+1))))},{x,y}]
{7.2912*^59,{x->2.89727*^60,y->2.93582*^60}}
I am not sure what to do. After reading about constrained optimization problem the syntax appears to be correct. Sorry, it this question is really basic I am very new to mathmatica, also since I am using a notebook I could not past the output from the first two lines in.
The constraint is incorrectly specified, it should be 1000 == 5 x + 5 y. Maximize works better with exact numbers.
Maximize[{Rationalize[y^(1 - 0.5) (x^0.5 (1 - 0.75 (x/(x + 1))))],
1000 == 5 x + 5 y}, {x, y}] // N
(* {25.7537, {x -> 96.97, y -> 103.03}} *)
How could I implement a program that takes in the two sides of a trig equation (could be generalized to anything but for now I'll leave it at just trig identities) and the program will output the steps to transform one side into another (or transform them both) to show that they are in fact equal. The program will assume that they are equal in the first place. I am quite stumped as to how I might implement an algorithm to do this. My first thought was something to do with graphs, but I couldn't think of anything beyond this. From there, I thought that I should first parse both sides of the equation into trees. For example (cot x * sin) / (sin x + cos x) would look like this:
division
/ \
* +
/ \ / \
cot sin sin cos
After this, I had two similar ideas, both of which have problems. The first idea was to pick the side with the least number of leaves and try to manipulate it into the other side by using equivalencies that would be represented by "tree regexs." Examples of these "tree regexs" would be csc = 1 / sin or cot = cos / sin (in tree form of course), etc. My second idea would be to pick the side with more leaves and try to find some expression that when multiplied by that expression would equal the other side. Using reciprocals this wouldn't be too bad, however, I would then have to prove that the thing I multiplied by equals 1. Again I am back to this "tree regex" thing.
The major flaw with both of these is in what order/how could I apply these substitutions. Will it just have to be a big mess of if statements or is there a more elegant solution? Is there actually a graph-based solution that I'm not seeing. What (if any) might be a good algorithm to prove trig identities.
To be clear I am not talking about the "solve for x" type problem such as tan(x)sin(x) = 5, find all values of x but rather prove that sqrt((1 + sin x) / (1 - sin x)) = sec x + tan x
This is a simple algorithm for deciding trigonometric identities that can be brought into the form polynomial(sin x, cos x) = 0 :
Get rid of tan x, cot x, sec x, ..., sin 2x, ... by the obvious substitutions (tan x -> (sin x)/(cos x), ..., sin 2x -> 2 (sin x) (cos x), ...)
Transform identity to polynomial by squaring (isolated) roots (getting rid of multiple roots in an identity can be tricky, though), multiplying with denominators and bringing all expanded terms to one side
Replace all terms cos^2 x in the polynomial (cos^3 x = (cos^2 x)(cos x), cos^4 x = (cos^2 x)(cos^2 x), ...) by 1 - sin^2 x and expand the polynomial.
Finally a polynomial without cos^2 x is computed. If it is identical to 0 the identity is proven, otherwise the identity does not hold.
Your example sqrt((1 + sin x)/(1 - sin x)) = sec x + tan x:
Using the substitutions sec x -> 1/(cos x) and tan x -> (sin x)/(cos x) we get
sqrt((1 + sin x)/(1 - sin x)) = 1/(cos x) + (sin x)/(cos x).
For brevity let us write s instead of sin x and c instead of cos x, which gives us:
sqrt((1 + s)/(1 - s)) = 1/c + s/c
Squaring the equation and multiplying both sides with (1 - s)c^2 we get
(1 + s)c^2 = (1 + s)^2(1 - s).
Expanding the parenthesis and bringing everthing to one side we get
c^2 - sc^2 + s^3 + s^2 - s - 1 = 0
Substituting c^2 = 1 - s^2 into the polynomial we get
(1 - s^2) - s(1 - s^2) + s^3 + s^2 - s - 1 which expands to 0.
Hence the identity is proven.
Look out for texts on computer algebra (which I haven't), I'm sure you'll find clever ideas there.
My approach would be a graph-based search, as I doubt that a linear application of transformations will reliably lead to a solution.
Express the whole equation as an expression-tree the way you already started, but including an "equals" node above.
For the search-graph view, take one expression-tree as one search-state. The search-target is a decidable expression-tree like 1=1 or 1=0. When searching (expanding a search-state), create the child states by applying equivalence transformations on your expression (regex-like sounds quite plausible to me). Define an evaluation function that counts the overall complexity of an expression (e.g. number of nodes in the expression-tree). Do a directed search minimizing the evaluation function (expanding the lowest-complexity expression first), thus simplifying the expression until you reach a decidable form.
Depending on the expressions, it's quite possible that an unrestricted search never terminates. I don't know how you'd handle that, maybe by limiting the allowed complexity of expressions to some multiple of the original one. That would reduce the risk of running indefinitely, but leave you with undecided cases.
I have a differential equation A*dx/dt + B(y-y0) = 0
Where x is a very complicated function of y.
How can I use Mathematica to rearrange y to get a function x in order to solve this?
Thanks
There are two or three different problems here that you might be asking:
Option 1: The subject line
First, if you really do have a function f[x] defined and you want to rearrange it, you would be doing something like this:
f[x_]=2+x+x^2;
Solve[y==f[x],x]
However, even here you should notice that inverse functions are not necessarily unique. There are two functions given, and the domain of each is only for y>=7/4.
Option 2: Solving a DE
Now, the equation you give is a differential equation. That is not the same as "rearranging a function y=f[x] into x=g[y]" because there are derivatives involved.
Mathematica has a built-in differential-equation solver:
DSolve[a y'[t] + b (y[t] - y0) == 0, y[t], t]
That will give you a function (in terms of constants $a,b,y_0$) that is the answer, and it will include the unspecified constant of integration.
Your system seems to refer to two functions, x(t) and y(t). You cannot solve one equation for two variables, so it is impossible to solve this (Mathematica or otherwise) without more information.
Option 3: Rearranging an expression
As a third alternative, if you are trying to rearrange this equation without solving the differential equation, you can do that:
Solve[a x'[t] + b(y[t]-y0)==0,x'[t]]
This will give you $x'(t)$ in terms of the other constants and the function $y(t)$, but in order to integrate this (i.e. to solve the differential equation) you will need to know more about y[t].
I hope this hasn't been asked before, if so I apologize.
EDIT: For clarity, the following notation will be used: boldface uppercase for matrices, boldface lowercase for vectors, and italics for scalars.
Suppose x0 is a vector, A and B are matrix functions, and f is a vector function.
I'm looking for the best way to do the following iteration scheme in Mathematica:
A0 = A(x0), B0=B(x0), f0 = f(x0)
x1 = Inverse(A0)(B0.x0 + f0)
A1 = A(x1), B1=B(x1), f1 = f(x1)
x2 = Inverse(A1)(B1.x1 + f1)
...
I know that a for-loop can do the trick, but I'm not quite familiar with Mathematica, and I'm concerned that this is the most efficient way to do it. This is a justified concern as I would like to define a function u(N):=xNand use it in further calculations.
I guess my questions are:
What's the most efficient way to program the scheme?
Is RecurrenceTable a way to go?
EDIT
It was a bit more complicated than I tought. I'm providing more details in order to obtain a more thorough response.
Before doing the recurrence, I'm having problems understanding how to program the functions A, B and f.
Matrices A and B are functions of the time step dt = 1/T and the space step dx = 1/M, where T and M are the number of points in the {0 < x < 1, 0 < t} region. This is also true for vector the function f.
The dependance of A, B and f on x is rather tricky:
A and B are upper and lower triangular matrices (like a tridiagonal matrix; I suppose we can call them multidiagonal), with defined constant values on their diagonals.
Given a point 0 < xs < 1, I need to determine it's representative xn in the mesh (the closest), and then substitute the nth row of A and B with the function v( x) (transposed, of course), and the nth row of f with the function w( x).
Summarizing, A = A(dt, dx, xs, x). The same is true for B and f.
Then I need do the loop mentioned above, to define u( x) = step[T].
Hope I've explained myself.
I'm not sure if it's the best method, but I'd just use plain old memoization. You can represent an individual step as
xstep[x_] := Inverse[A[x]](B[x].x + f[x])
and then
u[0] = x0
u[n_] := u[n] = xstep[u[n-1]]
If you know how many values you need in advance, and it's advantageous to precompute them all for some reason (e.g. you want to open a file, use its contents to calculate xN, and then free the memory), you could use NestList. Instead of the previous two lines, you'd do
xlist = NestList[xstep, x0, 10];
u[n_] := xlist[[n]]
This will break if n > 10, of course (obviously, change 10 to suit your actual requirements).
Of course, it may be worth looking at your specific functions to see if you can make some algebraic simplifications.
I would probably write a function that accepts A0, B0, x0, and f0, and then returns A1, B1, x1, and f1 - say
step[A0_?MatrixQ, B0_?MatrixQ, x0_?VectorQ, f0_?VectorQ] := Module[...]
I would then Nest that function. It's hard to be more precise without more precise information.
Also, if your procedure is numerical, then you certainly don't want to compute Inverse[A0], as this is not a numerically stable operation. Rather, you should write
A0.x1 == B0.x0+f0
and then use a numerically stable solver to find x1. Of course, Mathematica's LinearSolve provides such an algorithm.