I want to calculate whether a point, (x,y), is inside a rectangle which is determined by two points, (a,b) and (c,d).
If a<=c and b<=d, then it is simple:
a<=x&&x<=c&&b<=y&&y<=d
However, since it is unknown whether a<=c or b<=d, the code should be
(a<=x&&x<=c||c<=x&&x<=a)&&(b<=y&&y<=d||d<=y&&y<=b)
This code may work, but it is too long. I can write a function and use it, but I wonder if there's shorter way (and should be executed very fast - the code is called a lot) to write it.
One I can imagine is:
((c-x)*(x-a)>=0)&&((d-y)*(y-b)>=0)
Is there more clever way to do this?
(And, is there any good way to iterate from a from c?)
Swap the variables as needed so that a = xmin and b = ymin:
if a > c: swap(a,c)
if b > d: swap(b,d)
a <= x <= c and b <= y <= d
Shorter but slightly less efficient:
min(a,c) <= x <= max(a,c) and min(b,d) <= y <= max(b,d)
As always when optimizing you should profile the different options and compare hard numbers. Pipelining, instruction reordering, branch prediction, and other modern day compiler/processor optimization techniques make it non-obvious whether programmer-level micro-optimizations are worthwhile. For instance it used to be significantly more expensive to do a multiply than a branch, but this is no longer always the case.
I like the this:
((c-x)*(x-a)>=0)&&((d-y)*(y-b)>=0)
but with more whitespace and more symmetry:
(c-x)*(a-x) <= 0 && (d-y)*(b-y) <= 0
It's mathematically elegant, and probably the fastest too. You will need to measure to really determine which is the fastest. With modern pipelined processors, I would expect that straight-line code with the minimum number of operators will run fastest.
While sorting the (a, b) and (c, d) pairs as suggested in the accepted answer is probably the best solution in this case, an even better application of this method would probably be to elevate the a < b and c < d requirement to the level of the program-wide invariant. I.e. require that all rectangles in your program are created and maintained in this "normalized" form from the very beginning. Thus, inside your point-in-rectangle test function you should simply assert that a < b and c < d instead of wasting CPU resources on actually sorting them in every call.
Define intermediary variables i = min(a,b), j = min(c,d), k = max(a,b), l = max(c,d)
Then you only need i<=x && x<=k && j<=y && y<=l.
EDIT: Mind you, efficiency-wise it's probably better to use your "too long" code in a function.
Related
I'll make examples in Python, since I use Python, but the question is not about Python.
Lets say I want to increment a variable by specific value so that it stays in given boundaries.
So for increment and decrement I have these two functions:
def up (a, s, Bmax):
r = a + s
if r > Bmax : return Bmax
else : return r
def down (a, s, Bmin):
r = a - s
if r < Bmin : return Bmin
else : return r
Note: it is supposed that initial value of the variable "a" is already in boundaries (min <= a <= max) so additional initial checking does not belong to this function. What makes me curious, almost every program I made needs these functions.
The question is:
are those classified as some typical operations and have they specific names?
if yes, is there some correspondence to intrinsic processor functionality so it is optimised in some compilers?
Reason why I ask is pure curiousity, of course I cannot optimise it in Python and I know little about CPU architecture.
To be more specific, on a lower level for an unsigned 8-bit integer the increment would look I suppose like this:
def up (a, s, Bmax):
counter = 0
while True:
if counter == s : break
if a == Bmax : break
if a == 255 : break
a += 1
counter += 1
I know the latter would not make any sense in Python so treat it as my naive attempt to imagine low level code which adds the value in place. There are some nuances, e.g. signed, unsigned, but I was interested merely about unsigned integers since I came across it more often.
It is called saturation arithmetic, it has native support on DSPs and GPUs (not a random pair: both deals with signals).
For example the NVIDIA PTX ISA let the programmer chose if an addition is saturated or not
add.type d, a, b;
add{.sat}.s32 d, a, b; // .sat applies only to .s32
.sat
limits result to MININT..MAXINT (no overflow) for the size of the operation.
The TI TMS320C64x/C64x+ DSP has support for
Dual 16-bit saturated arithmetic operations
and instruction like sadd to perform a saturated add and even a whole register (Saturation Status Register) dedicated to collecting precise information about saturation while executing a sequence of instructions.
Even the mainstream x86 has support for saturation with instructions like vpaddsb and similar (including conversions).
Another example is the GLSL clamp function, used to make sure color values are not outside the range [0, 1].
In general if the architecture must be optimized for signal/media processing it has support for saturation arithmetic.
Much more rare is the support for saturation with arbitrary bounds, e.g. asymmetrical bounds, non power of two bounds, non word sized bounds.
However, saturation can be implemented easily as min(max(v, b), B) where v is the result of the unsaturated (and not overflowed) operation, b the lower bound and B the upper bound.
So any architecture that support finding the minimum and the maximum without a branch, can implement any form of saturation efficiently.
See also this question for a more real example of how saturated addition is implemented.
As a side note the default behavior is wrap around: for 8-bit quantities the sum 255 + 1 equals 0 (i.e. operations are modulo 28).
I am trying to find the minimum of a function using this algorithm.
It's not an optimal algorithm, but I don't care at the moment.
Also, you don't have to know how the algorithm works in order to reply, but if you're curious, I talk about it at the end of this post. It's really not that difficult.
Incriminated Algorithm
function result = fmin(f,a,b,max_error)
if abs(b-a) < max_error
result = (a+b)/2;
else
r1 = a+(b-a)*rand(1,1); r2 = a+(b-a)*rand(1,1);
c = min([r1,r2]); d = max([r1,r2]);
fc = f(c); fd = f(d);
if fc <= fd
b = d;
else
a = c;
end
result = fmin(f,a,b,max_error);
end
Now, the problem is this algorithm returns a minimum that is far from the actual minimum (computed via the matlab predefined function fminbnd) for more than max_error, if I use it with values of max_error <= 1e-10. This situation, form a theoretical standpoint is not possible.
Being recursive, the algorithm would never return if the condition abs(b-a) < max_error is never satisfied.
So, I think there is some error arising form the approximation of the numbers. At first, I thought that r1 or r2 where not computed properly. At some point, the two numbers would go out of the [a,b] interval, thus invalidating the hypothesis on which the algorithm is working.
To prove this, I modified the algorithm above to include a check on the interval that's computed at every iteration:
Incriminated Algorithm 2 [Check on the extremes]
function result = fmin(f,a,b,max_error)
if abs(b-a) < max_error
result = (a+b)/2;
else
r1 = a+(b-a)*rand(1,1); r2 = a+(b-a)*rand(1,1);
c = min([r1,r2]); d=max([r1,r2]);
% check that c and d are actually inside [a,b]
if ((c < a)||(d > b))
disp('Max precision reached');
result = (a+b)/2;
return;
end
fc = f(c); fd = f(d);
if fc <= fd
b = d;
else
a = c;
end
result = fmin(f,a,b,max_error);
end
But I don't get any additional output from the console.
So, I am thinking there is some error in the computation of f(c) or f(d), but I don't know how to prove it.
Question
Finally, my questions are
Do we, at this point, can be sure that the error is committed in the computation of either one of f(c) or f(d)?
Can we prove it with some line of code? Or better, can we write the algorithm so that it returns when it is supposed to?
How the algorithm works (not strictly inherent to the question)
It's an iterative algorithm. Basically, the idea is to generate a sequence of intervals containing the solution, starting from an initial interval [a,b] in which a given function f is unimodal.
At every step, we randomly choose two number c and d so that a <= c <= d <= b. Now, if we find that f(c) > f(d) it means we are sure that we can discard the values the function assumes before c as valid candidates for a minimum, because of the unimodality. So we restrict the interval and repeat the procedure in the interval [c,b]. On the contrary, if it's f(c) < f(d), we can discard the values from d to b, so we repeat the procedure in the interval [a,d].
At every iteration, the interval gets shorter. When its length is minor than the specified max_error value, the algorithm returns the medium point of the last interval as an approximation of the minimum value.
EDIT
I see there is one person that wants to close this question because it is too broad.
Please sir, can you elaborate in the comments?
This subdivision method only works in the special case that your function is (quasi-)convex (one local minimum, monotonically falling on the left, raising on the right). In the case of several local minima it will often converge to one of them, but it is by no means guaranteed that the algorithm finds the global minimum. The reduction from a to c resp. from b to d can jump over several local minima.
I have some simple and very basic questions here but yet I would like to have the wind up once forever so I decided to ask for.
Ok, here comes the code and the question within:
is something like
for (n=0;n<length;++n) countsc[n]=0;
countsc[x]=1; // x is something
better than something like
for (n=0;n<length;++n) {
if (n != x) countsc[n]=0;
else countsc[n]=1;
}
or also
for (n=0;n<length;++n) countsc[n]=(n != x ? 0 : 1);
in terms of performance and optimization (speed, cpu and memory usage)?
How would it be the convenient way one should measure it, for example, with Javascript and/or with PHP? Would the answer be generally valid for all programming languages or it may differ?
In a similar way, is something like
a=0;
if (condition == true) a=1;
generally better than
if (condition == true) a=1;
else a=0;
or also
a = (condition == true ? 1 : 0);
when condition is usually false?
Probably not the answer you're looking for, but in general, I don't think there would be a general way to figure this out from static analysis of code. This is not only going to vary by language, but also possibly by architecture you run it on. I suspect any half-decent compiler should optimize these so there is little/no difference, but that may be less likely for interpreted languages.
If it really is a performance critical section of code (and you will only know that by profiling), then the best answer you will get will be by profiling and comparing the two candidate code sections on your target architecture, using the relevant language.
Depending on the quality of the compiler (or interpreter, if appropriate),
for (n=0;n<length;++n) countsc[n]=0;
countsc[x]=1;
is easier to optimize, since there are no branches to (mis)predict. The minor duplication of setting countsc[x] twice is trivial compared to need to test n each iteration and the penalty for a missed branch prediction inside the loop of
for (n=0;n<length;++n) {
if (n != x) countsc[n]=0;
else countsc[n]=1;
}
In terms of branch prediction, your third example with the ternary operator is identical to the second.
However, unless this is a tight inner loop that is either executed frequently and/or for very large values of length, it's unlikely to matter which approach you use looking at the overall running time of your program.
for (n=0;n<length;++n) countsc[n]=0;
countsc[x]=1;
is the more performant option, assuming comparison and assignment have roughly the same cost.
Similarly,
if (condition) a=1; // boolean == true is not pretty
else a=0;
should be more performant; you only have one assignment and one jump, while you have up to two assignments and one jump with the alternative version. This version
a = (condition == true ? 1 : 0);
should be just as good, I expect it is compiled to the same code as the if-else version.
I would think
for (n = 0; n < x; ++n)
countsc[n] = 0;
countsc[x] = 1;
for (n = x + 1; n < length; ++n)
countsc[n] = 0;
Would be better than using the conditional, and you avoid rewriting your countsc[x]. Of course, everything is O(n) and you won't notice any change in the speed in your program.
Let
2|n, 3|n,..., p_i|n, p_ j|n,..., p_k|n
p_i < p_ j< ... < p_k
where all primes up to p_i divide n and
j > i+1
I want to write a code in Mathematica to find p_i and determine {2,3,5,...,p_i}.
thanks.
B = {};
n = 2^6 * 3^8 * 5^3 * 7^2 * 11 * 23 * 29;
For[i = 1, i <= k, i++,
If[Mod[n, Prime[i]] == 0, AppendTo[B, Prime[i]]
If[Mod[n, Prime[i + 1]] > 0, Break[]]]];
mep1= Max[B];
B
mep1
result is
{2,3,5,7,11}
11
I would like to write the code instead of B to get B[n], since I need to draw the graph of mep1[n] for given n.
If I understand your question and code correctly you want a list of prime factors of the integer n but only the initial part of that list which matches the initial part of the list of all prime numbers.
I'll first observe that what you've posted looks much more like C or one of its relatives than like Mathematica. In fact you don't seem to have used any of the power of Mathematica's in-built functions at all. If you want to really use Mathematica you need to start familiarising yourself with these functions; if that doesn't appeal stick to C and its ilk, it's a fairly useful programming language.
The first step I'd take is to get the prime factors of n like this:
listOfFactors = Transpose[FactorInteger[n]][[1]]
Look at the documentation for the details of what FactorInteger returns; here I'm using transposition and part to get only the list of prime factors and to drop their coefficients. You may not notice the use of the Part function, the doubled square brackets are the usual notation. Note also that I don't have Mathematica on this machine so my syntax may be a bit awry.
Next, you want only those elements of listOfFactors which match the corresponding elements in the list of all prime numbers. Do this in two steps. First, get the integers from 1 to k at which the two lists match:
matches = TakeWhile[Range[Length[listOfFactors]],(listOfFactors[[#]]==Prime[#])&]
and then
listOfFactors[[matches]]
I'll leave it to you to:
assemble these fragments into the function you want;
correct the syntactical errors I have made; and
figured out exactly what is going on in each (sub-)expression.
I make no warranty that this approach is the best approach in any general sense, but it makes much better use of Mathematica's intrinsic functionality than your own first try and will, I hope, point you towards better use of the system in future.
I have five values, A, B, C, D and E.
Given the constraint A + B + C + D + E = 1, and five functions F(A), F(B), F(C), F(D), F(E), I need to solve for A through E such that F(A) = F(B) = F(C) = F(D) = F(E).
What's the best algorithm/approach to use for this? I don't care if I have to write it myself, I would just like to know where to look.
EDIT: These are nonlinear functions. Beyond that, they can't be characterized. Some of them may eventually be interpolated from a table of data.
There is no general answer to this question. A solver finding the solution to any equation does not exist. As Lance Roberts already says, you have to know more about the functions. Just a few examples
If the functions are twice differentiable, and you can compute the first derivative, you might try a variant of Newton-Raphson
Have a look at the Lagrange Multiplier Method for implementing the constraint.
If the function F is continuous (which it probably is, if it is an interpolant), you could also try the Bisection Method, which is a lot like binary search.
Before you can solve the problem, you really need to know more about the function you're studying.
As others have already posted, we do need some more information on the functions. However, given that, we can still try to solve the following relaxation with a standard non-linear programming toolbox.
min k
st.
A + B + C + D + E = 1
F1(A) - k = 0
F2(B) - k = 0
F3(C) -k = 0
F4(D) - k = 0
F5(E) -k = 0
Now we can solve this in any manner we wish, such as penalty method
min k + mu*sum(Fi(x_i) - k)^2
st
A+B+C+D+E = 1
or a straightforward SQP or interior-point method.
More details and I can help advise as to a good method.
m
The functions are all monotonically increasing with their argument. Beyond that, they can't be characterized. The approach that worked turned out to be:
1) Start with A = B = C = D = E = 1/5
2) Compute F1(A) through F5(E), and recalculate A through E such that each function equals that sum divided by 5 (the average).
3) Rescale the new A through E so that they all sum to 1, and recompute F1 through F5.
4) Repeat until satisfied.
It converges surprisingly fast - just a few iterations. Of course, each iteration requires 5 root finds for step 2.
One solution of the equations
A + B + C + D + E = 1
F(A) = F(B) = F(C) = F(D) = F(E)
is to take A, B, C, D and E all equal to 1/5. Not sure though whether that is what you want ...
Added after John's comment (thanks!)
Assuming the second equation should read F1(A) = F2(B) = F3(C) = F4(D) = F5(E), I'd use the Newton-Raphson method (see Martijn's answer). You can eliminate one variable by setting E = 1 - A - B - C - D. At every step of the iteration you need to solve a 4x4 system. The biggest problem is probably where to start the iteration. One possibility is to start at a random point, do some iterations, and if you're not getting anywhere, pick another random point and start again.
Keep in mind that if you really don't know anything about the function then there need not be a solution.
ALGENCAN (part of TANGO) is really nice. There are Python bindings, too.
http://www.ime.usp.br/~egbirgin/tango/codes.php - " general nonlinear programming that does not use matrix manipulations at all and, so, is able to solve extremely large problems with moderate computer time. The general algorithm is of Augmented Lagrangian type ... "
http://pypi.python.org/pypi/TANGO%20Project%20-%20ALGENCAN/1.0
Google OPTIF9 or ALLUNC. We use these for general optimization.
You could use standard search technic as the others mentioned. There are a few optimization you could make use of it while doing the search.
First of all, you only need to solve A,B,C,D because 1-E = A+B+C+D.
Second, you have F(A) = F(B) = F(C) = F(D), then you can search for A. Once you get F(A), you could solve B, C, D if that is possible. If it is not possible to solve the functions, you need to continue search each variable, but now you have a limited range to search for because A+B+C+D <= 1.
If your search is discrete and finite, the above optimizations should work reasonable well.
I would try Particle Swarm Optimization first. It is very easy to implement and tweak. See the Wiki page for it.