the decimal type takes 128 bits, so it is not naturally an atomic write.
I tried:
Interlocked.Exchange(ref myField, some new value)
but then I saw that decimal is not part of the supported types with Interlocked.Exchange.
I was thinking that doing a lock may be a little bit heavy for this write. Are there any other options?
As you said, Interlocked.Exchange can only work with 32bit or 64bit values, so it does not support decimal. Aside from using locks, one suggestion from a related C# StackOverflow post is to wrap the value in an object and then use Interlocked.Exchange to swap the object references. In F#, this would look like this:
type Boxed<'T when 'T : struct>(v:'T) =
member x.Value = v
let mutable d1 = Boxed(1M)
let d2 = Boxed(2M)
Interlocked.Exchange(&d1, d2)
The question is whether the overhead of an additional instance is greater than the overhead of using lock - I think this will depend on your specific case. If you have just a few decimals that you're working with, the extra objects may not be such a big deal, but you'll probably need to run some tests to find out.
I am implementing an algorithm using Data.Ratio (convergents of continued fractions).
However, I encounter two obstacles:
The algorithm starts with the fraction 1%0 - but this throws a zero denominator exception.
I would like to pattern match the constructor a :% b
I was exploring on hackage. An in particular the source seems to be using exactly these features (e.g. defining infinity = 1 :% 0, or pattern matching for numerator).
As beginner, I am also confused where it is determined that (%), numerator and such are exposed to me, but not infinity and (:%).
I have already made a dirty workaround using a tuple of integers, but it seems silly to reinvent the wheel about something so trivial.
Also would be nice to learn how read the source which functions are exposed.
They aren't exported precisely to prevent people from doing stuff like this. See, the type
data Ratio a = a:%a
contains too many values. In particular, e.g. 2/6 and 3/9 are actually the same number in ℚ and both represented by 1:%3. Thus, 2:%6 is in fact an illegal value, and so is, sure enough, 1:%0. Or it might be legal but all functions know how to treat them so 2:%6 is for all observable means equal to 1:%3 – I don't in fact know which of these options GHC chooses, but at any rate it's an implementation detail and could change in future releases without notice.
If the library authors themselves use such values for e.g. optimisation tricks that's one thing – they have after all full control over any algorithmic details and any undefined behaviour that could arise. But if users got to construct such values, it would result in brittle code.
So – if you find yourself starting an algorithm with 1/0, then you should indeed not use Ratio at all there but simply store numerator and denominator in a plain tuple, which has no such issues, and only make the final result a Ratio with %.
I'm taking a course on coursera that uses minizinc. In one of the assignments, I was spinning my wheels forever because my model was not performing well enough on a hidden test case. I finally solved it by changing the following types of accesses in my model
from
constraint sum(neg1,neg2 in party where neg1 < neg2)(joint[neg1,neg2]) >= m;
to
constraint sum(i,j in 1..u where i < j)(joint[party[i],party[j]]) >= m;
I dont know what I'm missing, but why would these two perform any differently from eachother? It seems like they should perform similarly with the former being maybe slightly faster, but the performance difference was dramatic. I'm guessing there is some sort of optimization that the former misses out on? Or, am I really missing something and do those lines actually result in different behavior? My intention is to sum the strength of every element in raid.
Misc. Details:
party is an array of enum vars
party's index set is 1..real_u
every element in party should be unique except for a dummy variable.
solver was Gecode
verification of my model was done on a coursera server so I don't know what optimization level their compiler used.
edit: Since minizinc(mz) is a declarative language, I'm realizing that "array accesses" in mz don't necessarily have a direct corollary in an imperative language. However, to me, these two lines mean the same thing semantically. So I guess my question is more "Why are the above lines different semantically in mz?"
edit2: I had to change the example in question, I was toting the line of violating coursera's honor code.
The difference stems from the way in which the where-clause "a < b" is evaluated. When "a" and "b" are parameters, then the compiler can already exclude the irrelevant parts of the sum during compilation. If "a" or "b" is a variable, then this can usually not be decided during compile time and the solver will receive a more complex constraint.
In this case the solver would have gotten a sum over "array[int] of var opt int", meaning that some variables in an array might not actually be present. For most solvers this is rewritten to a sum where every variable is multiplied by a boolean variable, which is true iff the variable is present. You can understand how this is less efficient than an normal sum without multiplications.
CLP(FD) allows user to set the domain for every wannabe-integer variable, so it's able to solve equations.
So far so good.
However you can't do the same in CLP(R) or similar languages (where you can do only simple inferences). And it's not hard to understand why: the fractional part of a number may have an almost infinite region, putted down by an implementation limit. This mean the search space will be too large to make any practical use for a solver which deals with floats like with integers. So it's the user task to write generator in CLP(R) and set constraint guards where needed to get variables instantiated with numbers (if simple inference is not possible).
So my question here: is there any CLP(FD)-like language over reals? I think it could be implemented by use of number rounding, searching and following incremental approximation.
There are at least some major CLP(FD) solvers that support real (decision) variables:
Gecode
JaCoP
ECLiPSe CLP (ic library)
Choco (using Ibex)
(The first three also support var float in MiniZinc.)
The answser to your question is yes. There is Constraint-based Solvers dedicated for floating numbers. I do not have a list of solvers but I know that that ibex http://www.ibex-lib.org is a library allowing the use of floats. You should also have a look at SMT-Solvers implementing the Real-Theory (http://smtlib.cs.uiowa.edu/solvers.shtml).
Most programming languages give 2 as the answer to square root of 4. However, there are two answers: 2 and -2. Is there any particular reason, historical or otherwise, why only one answer is usually given?
Because:
In mathematics, √x commonly, unless otherwise specified, refers to the principal (i.e. positive) root of x [http://mathworld.wolfram.com/SquareRoot.html].
Some languages don't have the ability to return more than one value.
Since you can just apply negation, returning both would be redundant.
If the square root method returned two values, then one of those two would practically always be discarded. In addition to wasting memory and complexity on the extra return value, it would be little used. Everyone knows that you can multiple the answer returned by -1 and get the other root.
I expect that only mathematical languages would return multiple values here, perhaps as an array or matrix. But for most general-purpose programming languages, there is negligible gain and non-negligible cost to doing as you suggest.
Some thoughts:
Historically, functions were defined as procedures which returned a single value.
It would have been fiddly (using primitive programming constructs) to define a clean function which returned multiple values like this.
There are always exceptions to the rule:
0 for example only has a single root (0).
You cannot take the square root of a negative number (unless the language supports complex numbers). This could be treated as an exception (like "divide by 0") in languages which don't support imaginary numbers or the complex number system.
It is usually simple to deduce the 2 square roots (simply negate the value returned by the function). This was probably left as an exercise by the caller of the sqrt() function, if their domain depended on dealing with both the positive (+) and negative (-) roots.
It's easier to return one number than to return two. Most engineering decisions are made in this manner.
There are many functions which only return 1 answer from 2 or more possibilities. Arc tangent for example. The arc tangent of 1 is returned as 45 degrees, but it could also be 225 or even 405. As with many things in life and programming there is a convention we know and can rely on. Square root functions return positive values is one of them. It is up to us, the programmers, to keep in mind there are other solutions and to act on them if needed in code.
By the way this is a common issue in robotics when dealing with kinematics and inverse kinematics equations where there are multiple solutions of links positions corresponding to Cartesian positions.
In mathematics, by convention it's always assumed that you want the positive square root of something unless you explicitly say otherwise. The square root of four really is two. If you want the negative answer, put a negative sign in front. If you want both, put the plus-or-minus sign. Without this convention it would be impossible to write equations; you would never know what the person intended even if they did put a sign in front (because it could be the negative of the negative square root, for example). Also, how exactly would you write any kind of computer code involving mathematics if operators started returning two values? It would break everything.
The unfortunate exception to this convention is when solving for variables. In the following equation:
x^2 = 4
You have no choice but to consider both possible values for X. if you take the square root of both sides, you get x = 2 but now you must put in the plus or minus sign to make sure you aren't missing any possible solutions. Also, remember that in this case it's technically X that can be either plus or minus, not the square root of four.
Because multiple return types are annoying to implement. If you really need the other result, isn't it easy enough to just multiple the result by -1?
Because most programmers only want one answer.
It's easy enough to generate the negative value from the positive value if the caller wants it. For most code the caller only uses the positive value.
However, nowadays it's easy to return two values in many languages. In JavaScript:
var sqrts=function(x) {
var s=Math.sqrt(x);
if (s>0) {
return [s,-s];
} else {
return [0];
}
}
As long as the caller knows to iterate through the array that comes back, you're gold.
>sqrts(2)
[1.4142135623730951, -1.4142135623730951]
I think because the function is called "sqrt", and if you wanted multiple roots, you would have to call the function "sqrts", which doesn't exist, so you can't do it.
The more serious answer is that you're suggesting a specific instance of a larger issue. Many equations, and commonly inverse functions (including sqrt) have multiple possible solutions, such as arcsin, etc, and these are, in general, an issue. With arcsin, for example, should one return an infinite number of answers? See, for example, discussions about branch cuts.
Because it was historically defined{{citation needed}} as the function which gives the side length of a square of known surface. And length is positive in that context.
you can always tell what is the other number, so maybe it's not necessary to return both of them.
It's likely because when people use a calculator to figure out a square root, they only want the positive value.
Go one step further and ask why your calculator won't let you take the square root of a negative number. It's possible, using imaginary numbers, but the average user has absolutely zero use for this.
On imaginary numbers.