To clarify first:
2^3 = 8. That's equivalent to 2*2*2. Easy.
2^4 = 16. That's equivalent to 2*2*2*2. Also easy.
2^3.5 = 11.313708... Er, that's not so easy to grok.
Want I want is a simple algorithm which most clearly shows how 2^3.5 = 11.313708. It should preferably not use any functions apart from the basic addition, subtract, multiply, or divide operators.
The code certainly doesn't have to be fast, nor does it necessarily need to be short (though that would help). Don't worry, it can be approximate to a given user-specified accuracy (which should also be part of the algorithm). I'm hoping there will be a binary chop/search type thing going on, as that's pretty simple to grok.
So far I've found this, but the top answer is far from simple to understand on a conceptual level.
The more answers the merrier, so I can try to understand different ways of attacking the problem.
My language preference for the answer would be C#/C/C++/Java, or pseudocode for all I care.
Ok, let's implement pow(x, y) using only binary searches, addition and multiplication.
Driving y below 1
First, take this out of the way:
pow(x, y) == pow(x*x, y/2)
pow(x, y) == 1/pow(x, -y)
This is important to handle negative exponents and drive y below 1, where things start getting interesting. This reduces the problem to finding pow(x, y) where 0<y<1.
Implementing sqrt
In this answer I assume you know how to perform sqrt. I know sqrt(x) = x^(1/2), but it is easy to implement it just using a binary search to find y = sqrt(x) using y*y=x search function, e.g.:
#define EPS 1e-8
double sqrt2(double x) {
double a = 0, b = x>1 ? x : 1;
while(abs(a-b) > EPS) {
double y = (a+b)/2;
if (y*y > x) b = y; else a = y;
}
return a;
}
Finding the answer
The rationale is that every number below 1 can be approximated as a sum of fractions 1/2^x:
0.875 = 1/2 + 1/4 + 1/8
0.333333... = 1/4 + 1/16 + 1/64 + 1/256 + ...
If you find those fractions, you actually find that:
x^0.875 = x^(1/2+1/4+1/8) = x^(1/2) * x^(1/4) * x^(1/8)
That ultimately leads to
sqrt(x) * sqrt(sqrt(x)) * sqrt(sqrt(sqrt(x)))
So, implementation (in C++)
#define EPS 1e-8
double pow2(double x, double y){
if (x < 0 and abs(round(y)-y) < EPS) {
return pow2(-x, y) * ((int)round(y)%2==1 ? -1 : 1);
} else if (y < 0) {
return 1/pow2(x, -y);
} else if(y > 1) {
return pow2(x * x, y / 2);
} else {
double fraction = 1;
double result = 1;
while(y > EPS) {
if (y >= fraction) {
y -= fraction;
result *= x;
}
fraction /= 2;
x = sqrt2(x);
}
return result;
}
}
Deriving ideas from the other excellent posts, I came up with my own implementation. The answer is based on the idea that base^(exponent*accuracy) = answer^accuracy. Given that we know the base, exponent and accuracy variables beforehand, we can perform a search (binary chop or whatever) so that the equation can be balanced by finding answer. We want the exponent in both sides of the equation to be an integer (otherwise we're back to square one), so we can make accuracy any size we like, and then round it to the nearest integer afterwards.
I've given two ways of doing it. The first is very slow, and will often produce extremely high numbers which won't work with most languages. On the other hand, it doesn't use log, and is simpler conceptually.
public double powSimple(double a, double b)
{
int accuracy = 10;
bool negExponent = b < 0;
b = Math.Abs(b);
bool ansMoreThanA = (a>1 && b>1) || (a<1 && b<1); // Example 0.5^2=0.25 so answer is lower than A.
double accuracy2 = 1.0 + 1.0 / accuracy;
double total = a;
for (int i = 1; i < accuracy* b; i++) total = total*a;
double t = a;
while (true) {
double t2 = t;
for(int i = 1; i < accuracy; i++) t2 = t2 * t; // Not even a binary search. We just hunt forwards by a certain increment
if((ansMoreThanA && t2 > total) || (!ansMoreThanA && t2 < total)) break;
if (ansMoreThanA) t *= accuracy2; else t /= accuracy2;
}
if (negExponent) t = 1 / t;
return t;
}
This one below is a little more involved as it uses log(). But it is much quicker and doesn't suffer from the super-high number problems as above.
public double powSimple2(double a, double b)
{
int accuracy = 1000000;
bool negExponent= b<0;
b = Math.Abs(b);
double accuracy2 = 1.0 + 1.0 / accuracy;
bool ansMoreThanA = (a>1 && b>1) || (a<1 && b<1); // Example 0.5^2=0.25 so answer is lower than A.
double total = Math.Log(a) * accuracy * b;
double t = a;
while (true) {
double t2 = Math.Log(t) * accuracy;
if ((ansMoreThanA && t2 > total) || (!ansMoreThanA && t2 < total)) break;
if (ansMoreThanA) t *= accuracy2; else t /= accuracy2;
}
if (negExponent) t = 1 / t;
return t;
}
You can verify that 2^3.5 = 11.313708 very easily: check that 11.313708^2 = (2^3.5)^2 = 2^7 = 128
I think the easiest way to understand the computation you would actually do for this would be to refresh your understanding of logarithms - one starting point would be http://en.wikipedia.org/wiki/Logarithm#Exponentiation.
If you really want to compute non-integer powers with minimal technology one way to do that would be to express them as fractions with denominator a power of two and then take lots of square roots. E.g. x^3.75 = x^3 * x^(1/2) * x^(1/4) then x^(1/2) = sqrt(x), x^(1/4) = sqrt(sqrt(x)) and so on.
Here is another approach, based on the idea of verifying a guess. Given y, you want to find x such that x^(a/b) = y, where a and b are integers. This equation implies that x^a = y^b. You can calculate y^b, since you know both numbers. You know a, so you can - as you originally suspected - use binary chop or perhaps some numerically more efficient algorithm to solve x^a = y^b for x by simply guessing x, computing x^a for this guess, comparing it with y^b, and then iteratively improving the guess.
Example: suppose we wish to find 2^0.878 by this method. Then set a = 439, b = 500, so we wish to find 2^(439/500). If we set x=2^(439/500) we have x^500 = 2^439, so compute 2^439 and (by binary chop or otherwise) find x such that x^500 = 2^439.
Most of it comes down to being able to invert the power operation.
In other words, the basic idea is that (for example) N2 should be basically the "opposite" of N1/2 so that if you do something like:
M = N2
L = M1/2
Then the result you get in L should be the same as the original value in N (ignoring any rounding and such).
Mathematically, that means that N1/2 is the same as sqrt(N), N1/3 is the cube root of N, and so on.
The next step after that would be something like N3/2. This is pretty much the same idea: the denominator is a root, and the numerator is a power, so N3/2 is the square root of the cube of N (or the cube of the square root of N--works out the same).
With decimals, we're just expressing a fraction in a slightly different form, so something like N3.14 can be viewed as N314/100--the hundredth root of N raised to the power 314.
As far as how you compute these: there are quite a few different ways, depending heavily on the compromise you prefer between complexity (chip area, if you're implementing it in hardware) and speed. The obvious way is to use a logarithm: AB = Log-1(Log(A)*B).
For a more restricted set of inputs, such as just finding the square root of N, you can often do better than that extremely general method though. For example, the binary reducing method is quite fast--implemented in software, it's still about the same speed as Intel's FSQRT instruction.
As stated in the comments, its not clear if you want a mathematical description of how fractional powers work, or an algorithm to calculate fractional powers.
I will assume the latter.
For almost all functions (like y = 2^x) there is a means of approximating the function using a thing called the Taylor Series http://en.wikipedia.org/wiki/Taylor_series. This approximates any reasonably behaved function as a polynomial, and polynomials can be calculated using only multiplication, division, addition and subtraction (all of which the CPU can do directly). If you calculate the Taylor series for y = 2^x and plug in x = 3.5 you will get 11.313...
This almost certainly not how exponentiation is actually done on your computer. There are many algorithms which run faster for different inputs. For example, if you calculate 2^3.5 using the Taylor series, then you would have to look at many terms to calculate it with any accuracy. However, the Taylor series will converge much faster for x = 0.5 than for x = 3.5. So one obvious improvement is to calculate 2^3.5 as 2^3 * 2^0.5, as 2^3 is easy to calculate directly. Modern exponentiation algorithms will use many, many tricks to speed up processing - but the principle is still much the same, approximate the exponentiation function as some infinite sum, and calculate as many terms as you need to get the accuracy that is required.
Related
System.Numerics.BigInteger lets you multiply large integers together, but is there anything of the same type for floating point numbers? If not, is there a free library I can use?
//this but with floats
System.Numerics.BigInteger maxint = new BigInteger(int.MaxValue);
System.Numerics.BigInteger big = maxint * maxint * maxint;
System.Console.WriteLine(big);
Perhaps you're looking for BigRational? Microsoft released it under their BCL project on CodePlex. Not actually sure how or if it will fit your needs.
It keeps it as a rational number. You can get the a string with the decimal value either by casting or some multiplication.
var r = new BigRational(5000, 3768);
Console.WriteLine((decimal)r);
Console.WriteLine((double)r);
Or with a simple(ish) extension method like this:
public static class BigRationalExtensions
{
public static string ToDecimalString(this BigRational r, int precision)
{
var fraction = r.GetFractionPart();
// Case where the rational number is a whole number
if(fraction.Numerator == 0 && fraction.Denominator == 1)
{
return r.GetWholePart() + ".0";
}
var adjustedNumerator = (fraction.Numerator
* BigInteger.Pow(10, precision));
var decimalPlaces = adjustedNumerator / fraction.Denominator;
// Case where precision wasn't large enough.
if(decimalPlaces == 0)
{
return "0.0";
}
// Give it the capacity for around what we should need for
// the whole part and total precision
// (this is kinda sloppy, but does the trick)
var sb = new StringBuilder(precision + r.ToString().Length);
bool noMoreTrailingZeros = false;
for (int i = precision; i > 0; i--)
{
if(!noMoreTrailingZeros)
{
if ((decimalPlaces%10) == 0)
{
decimalPlaces = decimalPlaces/10;
continue;
}
noMoreTrailingZeros = true;
}
// Add the right most decimal to the string
sb.Insert(0, decimalPlaces%10);
decimalPlaces = decimalPlaces/10;
}
// Insert the whole part and decimal
sb.Insert(0, ".");
sb.Insert(0, r.GetWholePart());
return sb.ToString();
}
}
If it's out of the precision range of a decimal or double, they will be cast to their respective types with a value of 0.0. Also, casting to decimal, when the result is outside of its range, will cause an OverflowException to be thrown.
The extension method I wrote (which may not be the best way to calculate a fraction's decimal representation) will accurately convert it to a string, with unlimited precision. However, if the number is smaller than the precision requested, it will return 0.0, just like decimal or double would.
It should be considered what the implications would be if there were a BigFloat type.
BigFloat x = 1.0;
BigFloat y = 3.0;
BigFloat z = x / y;
The answer would be 0.333333333333333333333333333333333333333333333333333333 recurring. Forever. Infinite. Out of Memory Error.
It is easy to construct an infinite BigFloat.
However, if you are happy to stick to rational numbers, those express by the dividing one integer with another than you can use BigInteger to build a BigRational type that can provide arbitrary precision for representing any real number.
BigRational x = 1;
BigRational y = 3;
BigRational z = x / y;
This works and gives us this type:
You can just NuGet BigRational and you'll find many implementations, including once from Microsoft.
I have a simple algebraic relationship that uses three variables. I can guarantee that I know two of the three and need to solve for the third, but I don't necessarily know which two of the variables I will know. I'm looking for a single method or algorithm that can handle any of the cases without a huge batch of conditionals. This may not be possible, but I would like to implement it in a more general sense rather than code in every relationship in terms of the other variables.
For example, if this were the relationship:
3x - 5y + z = 5
I don't want to code this:
function(int x, int y)
{
return 5 - 3x + 5y;
}
function(int x, int z)
{
return (5 - z - 3x)/(-5);
}
And so on. Is there a standard sort of way to handle programming problems like this? Maybe using matrices, parameterization, etc?
If you restrict yourself to the kind of linear functions shown above, you could generalize the function like this
3x - 5y + z = 5
would become
a[0]*x[0] + a[1]*x[1] + a[2]*x[2] = c
with a = { 3, -5, 1 } and c = 5.
I.e., you need a list (or array) of constant factors List<double> a; and a list of variables List<double?> x; plus the constant on the right side double c;
public double Solve(IList<double> a, IList<double?> x, double c)
{
int unknowns = 0;
int unkonwnIndex = 0; // Initialization required because the compiler is not smart
// enough to infer that unknownIndex will be initialized when
// our code reaches the return statement.
double sum = 0.0;
if (a.Count != x.Count) {
throw new ArgumentException("a[] and x[] must have same length");
}
for (int i = 0; i < a.Count; i++) {
if (x[i].HasValue) {
sum += a[i] * x[i].Value;
} else {
unknowns++;
unknownIndex = i;
}
}
if (unknowns != 1) {
throw new ArgumentException("Exactly one unknown expected");
}
return (c - sum) / a[unknownIndex];
}
Example:
3x - 5y + z = 5
5 - (- 5y + z)
x = --------------
3
As seen in the example the solution consists of subtracting the sum of all terms except the unknown term from the constant and then to divide by the factor of the unknown. Therefore my solution memorizes the index of the unknown.
You can generalize with powers like this, assuming that you have the equation
a[0]*x[0]^p[0] + a[1]*x[1]^p[1] + a[2]*x[2]^p[2] = c
you need an additional parameter IList<int> p and the result becomes
return Math.Pow((c - sum) / a[unknownIndex], 1.0 / p[unknownIndex]);
as x ^ (1/n) is equal to nth-root(x).
If you use doubles for the powers, you will even be able to represent functions like
5
7*x^3 + --- + 4*sqrt(z) = 11
y^2
a = { 7, 5, 4 }, p = { 3, -2, 0.5 }, c = 11
because
1
x^(-n) = ---
x^n
and
nth-root(x) = x^(1/n)
However, you will not be able to find the roots of true non-linear polynomials like x^2 - 5x = 7. The algorithm shown above, works only, if the unknown appears exactly once in the equation.
Yes, here is one function:
private double? ValueSolved (int? x, int? y, int? z)
{
if (y.HasValue && z.HasValue && !x.HasValue
return (5 + (5 * y.Value) - z.Value) / 3;
if (x.HasValue && z.HasValue && !y.HasValue
return (5 - z.Value - (3 * x.Value)) / -5;
if (x.HasValue && y.HasValue && !z.HasValue
return 5 - (3 * x.Value) + (5 * y.Value);
return null;
}
There is no standard way of solving such a problem.
In the general case, symbolic math is a problem solved by purpose built libraries, Math.NET has a symbolic library you might be interested in: http://symbolics.mathdotnet.com/
Ironically, a much tougher problem, a system of linear equations, can be easily solved by a computer by calculating an inverse matrix. You can set up the provided equation in this manner, but there are no built-in general purpose Matrix classes in .NET.
In your specific case, you could use something like this:
public int SolveForVar(int? x, int? y, int? z)
{
int unknownCount = 0;
int currentSum = 0;
if (x.HasValue)
currentSum += 3 * x.Value;
else
unknownCount++;
if (y.HasValue)
currentSum += -5 * y.Value;
else
unknownCount++;
if (z.HasValue)
currentSum += z.Value;
else
unknownCount++;
if (unknownCount > 1)
throw new ArgumentException("Too Many Unknowns");
return 5 - currentSum;
}
int correctY = SolveForVar(10, null, 3);
Obviously that approach gets unwieldy for large variable counts, and doesn't work if you need lots of dynamic numbers or complex operations, but it could be generalized to a certain extent.
I'm not sure what you are looking for, since the question is tagged symbolic-math but the sample code you have is producing numerical solutions, not symbolic ones.
If you want to find a numerical solution for a more general case, then define a function
f(x, y, z) = 3x - 5y + z - 5
and feed it to a general root-finding algorithm to find the value of the unknown parameter(s) that will produce a root. Most root-finding implementations allow you to lock particular function parameters to fixed values before searching for a root along the unlocked dimensions of the problem.
I have two values: X = -78.0921 and Y = -64.6294. Now, when I want to compute Math.Pow(X, Y) it returns NaN. What should I do? How can I solve this problem?
How should I calculate this power? Is there any other function that can calculate this?...or maybe it is not defined mathematically ?
You've tried to compute a number that is not real.
By not real I mean, if we tried every single number between the largest number and the smallest number you can think of, none of those numbers is the solution to -78.0921 to the power of -64.6294.
In fact, no real number is the solution to -1 to the power of 0.5, or the square root of -1, and in general for a^b if a is negative and b is non-integer, the result is not real.
The inability to express such a useful result in real numbers lead to the invention of complex numbers. We say sqrt(-1) = i, the imaginary unit, in the complex number system - all complex numbers have a real component and an imaginary component, expressed as a + b*i.
In general, no negative number to a fractional power produces a real result, as it will have some component of i in it - the closer to a .5 the power is, the more i, the closer to a .0, the more real, and the path follows a circle between real and imaginary, e.g.
-1^x = cos(pi*x)+i*sin(pi*x)
Read more about complex numbers: http://en.wikipedia.org/wiki/Complex_number
If you wish to work with complex numbers in C#, try http://msdn.microsoft.com/en-us/library/system.numerics.complex.aspx
However, unless complex numbers have some meaning in your problem domain (they are meaningful in many electrical engineering, physics and signal analysis problems, for example) it's possible that your data is wrong or your logic is wrong to be attempting to do such a thing in the first place.
The documentation states that the returned value for those inputs is NaN.
x < 0 but not NegativeInfinity; y is not an integer, NegativeInfinity, or PositiveInfinity: returns NaN
The reason that NaN is returned is that the function is not well-defined for your input values. The Wikipedia article on Exponentiation covers this topic.
I think you mean that it returns NaN because your input matches the following:
x < 0 but not NegativeInfinity; y is not an integer, NegativeInfinity, or PositiveInfinity
Which is correct, as per the documentation.
Here is what I used for (native C# library System.Numerics):
Complex.Pow(x, y).Real;
The result is the same as:
double checkSquareRoot(double x, double y)
{
var result = Math.Pow(x, y);
if (x > 0)
{
return result;
}
else
{
return -1 * Math.Pow(-x, y);
}
}
Hope it helps!
I had a similar issue and handled as shown below, you have to adjust the min and max values as needed, in my case they are 0 and 10.
double alpha = FastMath.pow(weight, parameters.getAlpha());
if(alpha == Double.NEGATIVE_INFINITY) {
alpha = 0d;
}
if(alpha == Double.POSITIVE_INFINITY) {
alpha = 10d;
}
double beta = FastMath.pow(1d / distanceMatrix[row][column],
parameters.getBeta());
if(beta == Double.NEGATIVE_INFINITY) {
beta = 0d;
}
if(beta == Double.POSITIVE_INFINITY) {
beta = 10d;
}
It is another weak point in C#. We know that cubic root of -125 is equal to -5, but the result of Console.Write(Math.Pow(-125,1.0/3)); is NaN.
Perhaps you should try this:
if (x>0) {
Console.Write(Math.Pow(x,y));
}
else if (x<0) {
double x = Abs(x);
double z = Math.Pow(x,y);
if (y%2==0)
Console.Write(z);
else
Console.Write(-z);
}
I want to make
BigInteger.ModPow(1/BigInteger, 2,5);
but 1/BigInteger always return 0, which causes, that the result is 0 too. I tried to look for some BigDecimal class for c# but I have found nothing. Is there any way how to count this even if there is no BigDecimal?
1/a is 0 for |a|>1, since BigIntegers use integer division where the fractional part of a division is ignored. I'm not sure what result you're expecting for this.
I assume you want to modular multiplicative inverse of a modulo m, and not a fractional number. This inverse exists iff a and m are co-prime, i.e. gcd(a, m) = 1.
The linked wikipedia page lists the two standard algorithms for calculating the modular multiplicative inverse:
Extended Euclidean algorithm, which works for arbitrary moduli
It's fast, but has input dependent runtime.
I don't have C# code at hand, but porting the pseudo code from wikipedia should be straight forward.
Using Euler's theorem:
This requires knowledge of φ(m) i.e. you need to know the prime factors of m. It's a popular choice when m is a prime and thus φ(m) = m-1 when it simply becomes . If you need constant runtime and you know φ(m), this is the way to go.
In C# this becomes BigInteger.ModPow(a, phiOfM-1, m)
The overload of the / operator chosen, is the following:
public static BigInteger operator /(
BigInteger dividend,
BigInteger divisor
)
See BigInteger.Division Operator. If the result is between 0 and 1 (which is likely when dividend is 1 as in your case), because the return value is an integer, 0 is returned, as you see.
What are you trying to do with the ModPow method? Do you realize that 2,5 are two arguments, two and five, not "two-point-five"? Is your intention "take square modulo 5"?
If you want floating-point division, you can use:
1.0 / (double)yourBigInt
Note the cast to double. This may lose precision and even "underflow" to zero if yourBigInt is too huge.
For example you need to get d in the next:
3*d = 1 (mod 9167368)
this is equally:
3*d = 1 + k * 9167368, where k = 1, 2, 3, ...
rewrite it:
d = (1 + k * 9167368)/3
Your d must be the integer with the lowest k.
Let's write the formula:
d = (1 + k * fi)/e
public static int MultiplicativeInverse(int e, int fi)
{
double result;
int k = 1;
while (true)
{
result = (1 + (k * fi)) / (double) e;
if ((Math.Round(result, 5) % 1) == 0) //integer
{
return (int)result;
}
else
{
k++;
}
}
}
let's test this code:
Assert.AreEqual(Helper.MultiplicativeInverse(3, 9167368), 6111579); // passed
I understand that floating point arithmetic as performed in modern computer systems is not always consistent with real arithmetic. I am trying to contrive a small C# program to demonstrate this. eg:
static void Main(string[] args)
{
double x = 0, y = 0;
x += 20013.8;
x += 20012.7;
y += 10016.4;
y += 30010.1;
Console.WriteLine("Result: "+ x + " " + y + " " + (x==y));
Console.Write("Press any key to continue . . . "); Console.ReadKey(true);
}
However, in this case, x and y are equal in the end.
Is it possible for me to demonstrate the inconsistency of floating point arithmetic using a program of similar complexity, and without using any really crazy numbers? I would like, if possible, to avoid mathematically correct values that go more than a few places beyond the decimal point.
double x = (0.1 * 3) / 3;
Console.WriteLine("x: {0}", x); // prints "x: 0.1"
Console.WriteLine("x == 0.1: {0}", x == 0.1); // prints "x == 0.1: False"
Remark: based on this don't make the assumption that floating point arithmetic is unreliable in .NET.
Here's an example based on a prior question that demonstrates float arithmetic not working out exactly as you would think.
float f = (13.45f * 20);
int x = (int)f;
int y = (int)(13.45f * 20);
Console.WriteLine(x == y);
In this case, false is printed to the screen. Why? Because of where the math is performed versus where the cast to int is happening. For x, the math is performed in one statement and stored to f, then it is being cast to an integer. For y, the value of the calculation is never stored before the cast. (In x, some precision is lost between the calculation and the cast, not the case for y.)
For an explanation behind what's specifically happening in float math, see this question/answer. Why differs floating-point precision in C# when separated by parantheses and when separated by statements?
My favourite demonstration boils down to
double d = 0.1;
d += 0.2;
d -= 0.3;
Console.WriteLine(d);
The output is not 0.
Try making it so the decimal is not .5.
Take a look at this article here
http://floating-point-gui.de/
try sum VERY big and VERY small number. small one will be consumed and result will be same as large number.
Try performing repeated operations on an irrational number (such as a square root) or very long length repeating fraction. You'll quickly see errors accumulate. For instance, compute 1000000*Sqrt(2) vs. Sqrt(2)+Sqrt(2)+...+Sqrt(2).
The simplest I can think of right now is this:
class Test
{
private static void Main()
{
double x = 0.0;
for (int i = 0; i < 10; ++i)
x += 0.1;
Console.WriteLine("x = {0}, expected x = {1}, x == 1.0 is {2}", x, 1.0, x == 1.0);
Console.WriteLine("Allowing for a small error: x == 1.0 is {0}", Math.Abs(x - 1.0) < 0.001);
}
}
I suggest that, if you're truly interested, you take a look any one of a number of pages that discuss floating point numbers, some in gory detail. You will soon realize that, in a computer, they're a compromise, trading off accuracy for range. If you are going to be writing programs that use them, you do need to understand their limitations and problems that can arise if you don't take care. It will be worth your time.
double is accurate to ~15 digits. You need more precision to really start hitting problems with only a few floating point operations.