How to find the first perfect square from the function: f(n)=An²+Bn+C? B and C are given. A,B,C and n are always integer numbers, and A is always 1. The problem is finding n.
Example: A=1, B=2182, C=3248
The answer for the first perfect square is n=16, because sqrt(f(16))=196.
My algorithm increments n and tests if the square root is a integer nunber.
This algorithm is very slow when B or C is large, because it takes n calculations to find the answer.
Is there a faster way to do this calculation? Is there a simple formula that can produce an answer?
What you are looking for are integer solutions to a special case of the general quadratic Diophantine equation1
Ax^2 + Bxy + Cy^2 + Dx + Ey + F = 0
where you have
ax^2 + bx + c = y^2
so that A = a, B = 0, C = -1, D = b, E = 0, F = c where a, b, c are known integers and you are looking for unknown x and y that satisfy this equation. Once you recognize this, solutions to this general problem are in abundance. Mathematica can do it (use Reduce[eqn && Element[x|y, Integers], x, y]) and you can even find one implementation here including source code and an explanation of the method of solution.
1: You might recognize this as a conic section. It is, and people have been studying them for thousands of years. As such, our understanding of them is very deep and your problem is actually quite famous. The study of them is an immensely deep and still active area of mathematics.
Related
Given a prime number p, find a four integers such that p is equal to sum of square of those integers.
1 < p < 10^12.
If p is of form 8n + 1 or 8n + 5, then p can be written as sum of two squares. This can be solved in O(sqrt(p)*log(sqrt(p)). But for other cases,i.e. when p cannot be written as sum of two squares, than is very inefficient. So, it would be great if anyone can give some resource material which i can read to solve the problem.
Given your constraints, I think that you can do a smart brute force.
First, note that if p = a^2 + b^2 + c^2 + d^2, each of a, b, c, d have to be less than 10^6. So just loop over a from 0 to sqrt(p). Consider q = p - a^2. It is easy to check whether q can be written as the sum of three squares using Legendre's three-square theorem. Once you find a value of q that works, a is fixed and you can just worry about q.
Deal with q the same way. Loop over b from 0 to sqrt(q), and consider r = q - b^2. Fermat's two-square theorem tells you how to check whether r can be written as the sum of two squares. Though this check requires O(sqrt(r)) time again, in practice you should be able to quickly find a value of b that works.
After this, it should be straightforward to find a (c,d) pair that works for r.
Since the loops for finding a and b and (c,d) are not nested but come one after the other, the complexity should be low enough to work in your problem.
This is part of a bigger question. Its actually a mathematical problem. So it would be really great if someone can direct me to any algorithm to obtain the solution of this problem or a pseudo code will be of help.
The question. Given an equation check if it has an integral solution.
For example:
(26a+5)/32=b
Here a is an integer. Is there an algorithm to predict or find if b can be an integer. I need a general solution not specific to this question. The equation can vary. Thanks
Your problem is an example of a linear Diophantine equation. About that, Wikipedia says:
This Diophantine equation [i.e., a x + b y = c] has a solution (where x and y are integers) if and only if c is a multiple of the greatest common divisor of a and b. Moreover, if (x, y) is a solution, then the other solutions have the form (x + k v, y - k u), where k is an arbitrary integer, and u and v are the quotients of a and b (respectively) by the greatest common divisor of a and b.
In this case, (26 a + 5)/32 = b is equivalent to 26 a - 32 b = -5. The gcd of the coefficients of the unknowns is gcd(26, -32) = 2. Since -5 is not a multiple of 2, there is no solution.
A general Diophantine equation is a polynomial in the unknowns, and can only be solved (if at all) by more complex methods. A web search might turn up specialized software for that problem.
Linear Diophantine equations take the form ax + by = c. If c is the greatest common divisor of a and b this means a=z'c and b=z''c then this is Bézout's identity of the form
with a=z' and b=z'' and the equation has an infinite number of solutions. So instead of trial searching method you can check if c is the greatest common divisor (GCD) of a and b
If indeed a and b are multiples of c then x and y can be computed using extended Euclidean algorithm which finds integers x and y (one of which is typically negative) that satisfy Bézout's identity
(as a side note: this holds also for any other Euclidean domain, i.e. polynomial ring & every Euclidean domain is unique factorization domain). You can use Iterative Method to find these solutions:
Integral solution to equation `a + bx = c + dy`
Can someone please explain strassen's algorithm for matrix multiplication in an intuitive way? I've gone through (well, tried to go through) the explanation in the book and wiki but it's not clicking upstairs. Any links on the web that use a lot of English rather than formal notation etc. would be helpful, too. Are there any analogies which might help me build this algorithm from scratch without having to memorize it?
Consider multiplying two 2x2 matrices, as follows:
A B * E F = AE+BG AF+BH
C D G H CE+DG CF+DH
The obvious way to compute the right side is just to do the 8 multiplies and 4 additions. But imagine multiplies are a lot more expensive than additions, so we want to reduce the number of multiplications if at all possible. Strassen uses a trick to compute the right hand side with one less multiply and a lot more additions (and some subtractions).
Here are the 7 multiplies:
M1 = (A + D) * (E + H) = AE + AH + DE + DH
M2 = (A + B) * H = AH + BH
M3 = (C + D) * E = CE + DE
M4 = A * (F - H) = AF - AH
M5 = D * (G - E) = DG - DE
M6 = (C - A) * (E + F) = CE + CF - AE - AF
M7 = (B - D) * (G + H) = BG + BH - DG - DH
So to compute AE+BG, start with M1+M7 (which gets us the AE and BG terms), then add/subtract some of the other Ms until AE+BG is all we are left with. Miraculously, the M's are chosen so that M1+M7-M2+M5 works. Same with the other 3 results required.
Now just realize this works not just for 2x2 matrices, but for any (even) sized matrices where the A..H are submatrices.
In my opinion there are 3 ideas that you need to get:
You can split a matrix into blocks and operate on the resulting matrix of blocks like you would on a matrix of numbers. In particular you can multiply two such block matrices (of course as long as the number of block rows in one matches the number of block columns in the other) and get the same result as you would when multiplying original matrices of numbers.
The blocks necessary to express the result of 2x2 block matrix multiplication have enough common factors to allow computing them in fewer multiplications than the original formula implies. This is the trick described in Tony's answer.
Recursion.
Strassen algorithm is just an application of the above. To understand the analysis of its complexity, you need to read "Concrete Mathematics" by Ronald Graham, Donald Knuth, and Oren Patashnik or a similar book.
Took a quick look at the Wikipedia and it appears to me that this algorithm is a slight reduction in the number of multiplications required by rearranging the equations.
Here's an analogy. How many multiplications in x*x + 5*x + 6? Two, right? How many multiplications in (x+2)(x+3)? One, right? But they're the same expression!
Note, I do not expect this to provide a deep understanding of the algorithm, just an intuitive way in which you can understand how the algorithm can possibly lead to an improvement in calculation complexity.
You are given a list of distances between various points on a single line.
For example:
100 between a and b
20 between c and b
90 between c and d
170 between a and d
Return the sorted sequence of points as they appear on the line with distances between them:
For example the above input yields:
a<----80-----> c <----20------> b <----70-----> d or the reverse sequence(doesn't matter)
What is this problem called? I would like to research it.
If anybody knows, also, what are some of the possible asymptotic runtimes achieved for this?
not sure it has a name; more formally stated, it would be:
|a-b| = 100
|c-b| = 20
|c-d| = 90
|a-d| = 170
where |x| stands for the absolute value of x
As far as the generalized system goes, if you have N equations of this type with k unknowns, you have N choices of sign. Without loss of generality (because any solution yields a second solution with reversed ordering) you can choose a sign for the first equation, and a particular value for one of the unknowns (since the whole thing can slide left and right in position). Then you have 2N-1 possibilities for the remaining equations, and all you have to do is go through them to see which ones, if any, have solutions. Because the coefficients are all +/- 1 and each equation has 2 unknowns, you just go through them one by one:
Step 1: Without loss of generality,
choose a sign for one equation
and pick a value for one unknown:
a-b = 100, a = 0
Step 2: Choose signs for the remaining absolute values.
a = 0
a-b = 100
c-b = 20
c-d = 90
a-d = 170
Step 3: go through them one by one to solve / verify there aren't conflicts
(time = N steps).
0-b = 100 => b = -100
c-b = 20 => c = -80
c-d = 90 => d = -170
a-d = 170 => OK => (0,-100,-80,-170) is a solution
Step 4: if this doesn't work, go back through the possible choices of sign
and try again, starting at step 2.
Full set of solutions is (0,-100,-80,-170)
and its negation (0,100,80,170) and any number x<sub>0</sub> added to all terms.
So an upper bound for the runtime is O(N * 2N-1) ≡ O(N * 2N).
I suppose there could be a shortcut but no obvious one comes to mind.
As written, your problem is just a system of non-linear equations (expressed with absolute values or quadratic equations). However, it looks similar to the problems of finding Golomb rulers or perfect rulers.
If you consider your constraints as quadratic equations eg. (a-b)^2=100^2, then you can formulate this as a quadratic programming problem and use some of the well-studied techniques for that class of problem.
Considering the sign of the direction of each segment X[i] -> X[i+1] it becomes a boolean satisfiability problem. I can't see an obvious simplification. The runtime is O(2^N) - specifically 2^(N-2) tests with N values and an O(1) expression to test.
Assuming a = 0 and fixing the direction of a -> b:
a = 0
b = 100
c = b + 20 X[0] = 100 + 20 X[0]
d = c + 90 X[1] = 100 + 20 X[0] + 90 X[1]
test d == 170
where X[i] is either +1 or -1.
Although the expression for d appears to require O(N) operations ( (N-2) multiplications and (N-2) additions ), by using a Gray code or other such mechanism for changing the state of only one X at a time so the cost can be O(1) per test. ( though for N=4 it probably isn't worth it )
Simplifications may arise if you either have more constraints than points - if you were given |b-d| == 70, then you only need tests two cases rather than four - essentially b,c and d become their own fully constrained sub-problem.
Simplifications may also arise from the triangular property
| |a-b| - |b-c| | <= |a-c| <= |a-b| + |b-c| for all a, b and c.
So if you have many points, and you know the total of the distances between the points up to a certain point given the assignments made to X, and that total is further from the target value than the total of the distances between the remaining points, you can then deduce that there is no combination of assignments of the remaining points which will work.
algebra...
or it may be a simplification of the traveling salesman problem
I don't have an algorithms book handy, but this sounds like a graph search problem where the paths are constrained. You could probably use Dijkstra's Algorithm or some variant of it.
Using assorted matrix math, I've solved a system of equations resulting in coefficients for a polynomial of degree 'n'
Ax^(n-1) + Bx^(n-2) + ... + Z
I then evaulate the polynomial over a given x range, essentially I'm rendering the polynomial curve. Now here's the catch. I've done this work in one coordinate system we'll call "data space". Now I need to present the same curve in another coordinate space. It is easy to transform input/output to and from the coordinate spaces, but the end user is only interested in the coefficients [A,B,....,Z] since they can reconstruct the polynomial on their own. How can I present a second set of coefficients [A',B',....,Z'] which represent the same shaped curve in a different coordinate system.
If it helps, I'm working in 2D space. Plain old x's and y's. I also feel like this may involve multiplying the coefficients by a transformation matrix? Would it some incorporate the scale/translation factor between the coordinate systems? Would it be the inverse of this matrix? I feel like I'm headed in the right direction...
Update: Coordinate systems are linearly related. Would have been useful info eh?
The problem statement is slightly unclear, so first I will clarify my own interpretation of it:
You have a polynomial function
f(x) = Cnxn + Cn-1xn-1 + ... + C0
[I changed A, B, ... Z into Cn, Cn-1, ..., C0 to more easily work with linear algebra below.]
Then you also have a transformation such as: z = ax + b that you want to use to find coefficients for the same polynomial, but in terms of z:
f(z) = Dnzn + Dn-1zn-1 + ... + D0
This can be done pretty easily with some linear algebra. In particular, you can define an (n+1)×(n+1) matrix T which allows us to do the matrix multiplication
d = T * c ,
where d is a column vector with top entry D0, to last entry Dn, column vector c is similar for the Ci coefficients, and matrix T has (i,j)-th [ith row, jth column] entry tij given by
tij = (j choose i) ai bj-i.
Where (j choose i) is the binomial coefficient, and = 0 when i > j. Also, unlike standard matrices, I'm thinking that i,j each range from 0 to n (usually you start at 1).
This is basically a nice way to write out the expansion and re-compression of the polynomial when you plug in z=ax+b by hand and use the binomial theorem.
If I understand your question correctly, there is no guarantee that the function will remain polynomial after you change coordinates. For example, let y=x^2, and the new coordinate system x'=y, y'=x. Now the equation becomes y' = sqrt(x'), which isn't polynomial.
Tyler's answer is the right answer if you have to compute this change of variable z = ax+b many times (I mean for many different polynomials). On the other hand, if you have to do it just once, it is much faster to combine the computation of the coefficients of the matrix with the final evaluation. The best way to do it is to symbolically evaluate your polynomial at point (ax+b) by Hörner's method:
you store the polynomial coefficients in a vector V (at the beginning, all coefficients are zero), and for i = n to 0, you multiply it by (ax+b) and add Ci.
adding Ci means adding it to the constant term
multiplying by (ax+b) means multiplying all coefficients by b into a vector K1, multiplying all coefficients by a and shifting them away from the constant term into a vector K2, and putting K1+K2 back into V.
This will be easier to program, and faster to compute.
Note that changing y into w = cy+d is really easy. Finally, as mattiast points out, a general change of coordinates will not give you a polynomial.
Technical note: if you still want to compute matrix T (as defined by Tyler), you should compute it by using a weighted version of Pascal's rule (this is what the Hörner computation does implicitely):
ti,j = b ti,j-1 + a ti-1,j-1
This way, you compute it simply, column after column, from left to right.
You have the equation:
y = Ax^(n-1) + Bx^(n-2) + ... + Z
In xy space, and you want it in some x'y' space. What you need is transformation functions f(x) = x' and g(y) = y' (or h(x') = x and j(y') = y). In the first case you need to solve for x and solve for y. Once you have x and y, you can substituted those results into your original equation and solve for y'.
Whether or not this is trivial depends on the complexity of the functions used to transform from one space to another. For example, equations such as:
5x = x' and 10y = y'
are extremely easy to solve for the result
y' = 2Ax'^(n-1) + 2Bx'^(n-2) + ... + 10Z
If the input spaces are linearly related, then yes, a matrix should be able to transform one set of coefficients to another. For example, if you had your polynomial in your "original" x-space:
ax^3 + bx^2 + cx + d
and you wanted to transform into a different w-space where w = px+q
then you want to find a', b', c', and d' such that
ax^3 + bx^2 + cx + d = a'w^3 + b'w^2 + c'w + d'
and with some algebra,
a'w^3 + b'w^2 + c'w + d' = a'p^3x^3 + 3a'p^2qx^2 + 3a'pq^2x + a'q^3 + b'p^2x^2 + 2b'pqx + b'q^2 + c'px + c'q + d'
therefore
a = a'p^3
b = 3a'p^2q + b'p^2
c = 3a'pq^2 + 2b'pq + c'p
d = a'q^3 + b'q^2 + c'q + d'
which can be rewritten as a matrix problem and solved.