Related
I wrote a sieve using akka streams to find prime members of an arbitrary source of Int:
object Sieve extends App {
implicit val system = ActorSystem()
implicit val mat = ActorMaterializer(ActorMaterializerSettings(system))
implicit val ctx = implicitly[ExecutionContext](system.dispatcher)
val NaturalNumbers = Source.fromIterator(() => Iterator.from(2))
val IsPrimeByEurithmethes: Flow[Int, Int, _] = Flow[Int].filter {
case n: Int =>
(2 to Math.floor(Math.sqrt(n)).toInt).par.forall(n % _ != 0)
}
NaturalNumbers.via(IsPrimeByEurithmethes).throttle(100000, 1 second, 100000, ThrottleMode.Shaping).to(Sink.foreach(println)).run()
}
Ok, so this appears to work decently well. However, there are at least a few potential areas of concern:
The modulo checks are run using par.forall, ie they are totally hidden within the Flow that filters, but I can see how it would be useful to have a Map from the candidate n to another Map of each n % _. Maybe.
I am checking way too many of the candidates needlessly - both in terms of checking n that I will already know are NOT prime based on previous results, and by checking n % _ that are redundant. In fact, even if I think the n is prime, it suffices to check only the known primes up until that point.
The second point is my more immediate concern.
I think I can prove rather easily that there is a more efficient way - by filtering out the source given each NEW prime.
So then....
2, 3, 4, 5, 6, 7, 8, 9, 10, 11... => (after finding p=2)
2, 3, 5, 7, 9, , 11... => (after finding p=3)
2, 3, 5, 7, , 11... => ...
Now after finding a p and filtering the source, we need to know whether the next candidate is a p. Well, we can say for sure it is prime if the largest known prime is greater than its root, which will Always happen I believe, so it suffices to just pick the next element...
2, 3, 4, 5, 6, 7, 8, 9, 10, 11... => (after finding p=2) PICK n(2) = 3
2, 3, 5, 7, 9, , 11... => (after finding p=3) PICK n(3) = 5
2, 3, 5, 7, , 11... => (after finding p=5) PICK n(5) = 7
This seems to me like a rewriting of the originally-provided sieve to do far fewer checks at the cost of introducing a strict sequential dependency.
Another idea - I could remove the constraint by working things out in terms of symbols, like the minimum set of modulo checks that necessitate primality, etc.
Am I barking up the wrong tree? IF not, how can I go about messing with my source in this manner?
I just started fiddling around with akka streams recently so there might be better solutions than this (especially since the code feels kind of clumsy to me) - but your second point seemed to be just the right challenge for me to try out building a feedback loop within akka streams.
Find my full solution here: https://gist.github.com/MartinHH/de62b3b081ccfee4ae7320298edd81ee
The main idea was to accumulate the primes that are already found and merge them with the stream of incoming natural numbers so the primes-check could be done based on the results up to N like this:
def isPrime(n: Int, primesSoFar: SortedSet[Int]): Boolean =
!primesSoFar.exists(n % _ == 0) &&
!(primesSoFar.lastOption.getOrElse(2) to Math.floor(Math.sqrt(n)).toInt).par.exists(n % _ == 0)
This problem has an easy solution if our target time complexity is O(|V| * |E|) or O(V^3) and the like. However, my professor recently gave us an assignment with the problem statement being:
Let G = (V, E) be a connected undirected graph. Write an algorithm that determines if G contains a triangle in O(|V| + |E|).
At this point, I'm stumped. Wikipedia says:
It is possible to test whether a graph with m edges is triangle-free in time O(m^1.41).
There was no mention of the possibility for a faster algorithm besides one that runs on a Quantum computer. I started resorting to better sources afterwards. A question on Math.SE linked me to this paper that says:
The fastest algorithm known for finding and counting triangles relies on fast matrix product and has an O(n^ω) time complexity, where ω < 2.376 is the fast matrix product exponent.
And that's where I started to realize that maybe, we're being tricked into working on an unsolved problem! That dastardly professor!
However, I'm still a bit skeptical. The paper says "finding and counting". Is that equivalent to the problem I'm trying to solve?
TL;DR: Am I being fooled, or am I overlooking something so trivial?
Well, it turns out, this really isn't doable in O(|V| + |E|). Or at least, we don't know. I read 4 papers to reach this result. I stopped half-way into one of them, because I realized it was more focused on distributed computing than graph theory. One of them even gave probabilistic algorithms to determine triangle-freeness in "almost linear" time. The three relevant papers are:
Finding and counting given length cycles by Alon, Yuster & Zwick.
Testing Triangle-Freeness in General Graphs by Alon, Kaufman, Krivelevich & Ron.
Main-memory Triangle Computations for Very Large (Sparse (Power-Law)) Graphs by Latapy
I wrote about 2 pages of LaTeX for the assignment, quoting the papers with proper citations. The relevant statements in the papers are boxed:
In the end, I spoke to my professor and it turns out, it was in fact an unintended dire mistake. He then changed the required complexity to O(|V| * |E|). I don't blame him, he got me to learn more graph theory!
Here's the code for the O(|E|*|V|) version.
When you constrain |V| the bit mask intersect-any operation is effectively O(1) which gets you O(|E|), but that's cheating.
Realistically the complexity is O(|E| * (|V| / C)) where C is some architecture specific constant (i.e: 32, 64, 128).
function hasTriangle(v, e) {
if(v.length > 32) throw Error("|V| too big, we can't pretend bit mask intersection is O(1) if |V| is too big!");
// setup search Array
var search = new Uint32Array(v.length);
// loop through edges O(|E|)
var lastEdge = [-1, -1];
for(var i=0, l=e.length; i < l; i++) {
var edge = e[i];
if(edge[0] == lastEdge[0]) {
search[lastEdge[1]] = search[lastEdge[1]] | (1 << edge[0]);
search[edge[1]] = search[edge[1]] | (1 << edge[0]);
} else {
lastEdge = edge;
}
// bit mask intersection-any O(1), but unfortunately considered O(|V|)
if( (search[edge[0]] & search[edge[1]]) > 0 ) {
return true;
}
}
return false;
}
var V = [0, 1, 2, 3, 4, 5];
var E_no_triangle = [[0, 4], [0, 5], [1, 2], [1, 3], [2, 5]];
var E_triangle = [[0, 1], [0, 2], [0, 3], [1, 4], [2, 1], [2, 3], [4, 5]]; // Triange(0, 2, 3)
console.log(hasTriangle(V, E_no_triangle)); // false
console.log(hasTriangle(V, E_triangle)); // true
Order of operation in each recursive step of a backtracking algorithms are how much important in terms of the efficiency of that particular algorithm?
For Ex.
In the Knight’s Tour problem.
The knight is placed on the first block of an empty board and, moving
according to the rules of chess, must visit each square exactly once.
In each step there are 8 possible (in general) ways to move.
int xMove[8] = { 2, 1, -1, -2, -2, -1, 1, 2 };
int yMove[8] = { 1, 2, 2, 1, -1, -2, -2, -1 };
If I change this order like...
int xmove[8] = { -2, -2, 2, 2, -1, -1, 1, 1};
int ymove[8] = { -1, 1,-1, 1, -2, 2, -2, 2};
Now,
for a n*n board
upto n=6
both the operation order does not affect any visible change in the execution time,
But if it is n >= 7
First operation (movement) order's execution time is much less than the later one.
In such cases, it is not feasible to generate all the O(m!) operation order and test the algorithm. So how do I determine the performance of such algorithms on a specific movement order, or rather how could it be possible to reach one (or a set) of operation orders such that the algorithm that is more efficient in terms of execution time.
This is an interesting problem from a Math/CS perspective. There definitely exists a permutation (or set of permutations) that would be most efficient for a given n . I don't know if there is a permutation that is most efficient among all n. I would guess not. There could be a permutation that is better 'on average' (however you define that) across all n.
If I was tasked to find an efficient permutation I might try doing the following: I would generate a fixed number x of randomly generated move orders. Measure their efficiency. For every one of the randomly generated movesets, randomly create a fixed number of permutations that are near the original. Compute their efficiencies. Now you have many more permutations than you started with. Take top x performing ones and repeat. This will provide some locally maxed algorithms, but I don't know if it leads up to the globally maxed algorithm(s).
Given a set of numbers and a set of binary operations,
what is the fastest way to create random expression trees or exhaustively check every possible combination in Mathematica?
What I am trying to solve is given:
numbers={25,50,75,100,3,6} (* each can ONLY be used ONCE *)
operators={Plus,Subtract,Times,Divide} (* each can be used repeatedly *)
target=99
find expression trees that would evaluate to target.
I have two solutions whose performances I give for the case where expression trees contain exactly 4 of the numbers and 3 operators:
random sample & choice: ~25K trees / second
exhaustive scan: 806400 trees in ~2.15 seconds
(timed on a laptop with: Intel(R) Core(TM)2 Duo CPU T9300 # 2.50GHz, 3GB ram, no parallelization used yet but would be most welcome in answers)
My notebooks are a bit messy at the moment. So I would first love to pose the question and hope for original ideas and answers while I clean up my code for sharing.
Largest possible case is where every expression tree uses up all the (6) numbers and 'Length[numbers]-1' (5) operators.
Performance of methods in the largest case is:
random sample & choice: ~21K trees / second
exhaustive scan: 23224320 trees in ~100 seconds
Also I am using Mathematica 8.0.1 so I am more than all ears if there are any ways to do it in OpenCL or using compiled functions wiht CompilationTarget->"C", etc.
OK, this is not elegant or fast, and it's buggy, but it works (sometimes). It uses a monte carlo method, implementing the metropolis algorithm for a weight function that I (arbitrarily) selected just to see if this would work. This was some time ago for a similar problem; I suppose my mathematica skills have improved as it looks ugly now, but I have no time to fix it at the moment.
Execute this (it looks more reasonable when you paste it into a notebook):
ClearAll[swap];
swap[lst_, {p1_, p2_}] :=
ReplacePart[
lst, {p1 \[Rule] lst\[LeftDoubleBracket]p2\[RightDoubleBracket],
p2 \[Rule] lst\[LeftDoubleBracket]p1\[RightDoubleBracket]}]
ClearAll[evalops];
(*first element of opslst is Identity*)
evalops[opslst_, ord_, nums_] :=
Module[{curval}, curval = First#nums;
Do[curval =
opslst\[LeftDoubleBracket]p\[RightDoubleBracket][curval,
nums\[LeftDoubleBracket]ord\[LeftDoubleBracket]p\
\[RightDoubleBracket]\[RightDoubleBracket]], {p, 2, Length#nums}];
curval]
ClearAll[randomizeOrder];
randomizeOrder[ordlst_] :=
swap[ordlst, RandomInteger[{1, Length#ordlst}, 2]]
ClearAll[randomizeOps];
(*never touch the first element*)
randomizeOps[oplst_, allowedOps_] :=
ReplacePart[
oplst, {RandomInteger[{2, Length#oplst}] \[Rule] RandomChoice[ops]}]
ClearAll[takeMCstep];
takeMCstep[goal_, opslst_, ord_, nums_, allowedops_] :=
Module[{curres, newres, newops, neword, p},
curres = evalops[opslst, ord, nums];
newops = randomizeOps[opslst, allowedops];
neword = randomizeOrder[ord];
newres = evalops[newops, neword, nums];
Switch[Abs[newres - goal],
0, {newops,
neword}, _, (p = Abs[curres - goal]/Abs[newres - goal];
If[RandomReal[] < p, {newops, neword}, {opslst, ord}])]]
then to solve your actual problem, do
ops = {Times, Plus, Subtract, Divide}
nums = {25, 50, 75, 100, 3, 6}
ord = Range[Length#nums]
(*the first element is identity to simplify the logic later*)
oplist = {Identity}~Join~RandomChoice[ops, Length#nums - 1]
out = NestList[
takeMCstep[
99, #\[LeftDoubleBracket]1\[RightDoubleBracket], #\
\[LeftDoubleBracket]2\[RightDoubleBracket], nums, ops] &, {oplist,
ord}, 10000]
and then to see that it worked,
ev = Map[evalops[#\[LeftDoubleBracket]1\[RightDoubleBracket], #\
\[LeftDoubleBracket]2\[RightDoubleBracket], nums] &, out];
ev // Last // N
ev // ListPlot[#, PlotMarkers \[Rule] None] &
giving
thus, it obtained the correct order of operators and numbers after around 2000 tries.
As I said, it's ugly, inefficient, and badly programmed as it was a quick-and-dirty adaptation of a quick-and-dirty hack. If you're interested I can clean up and explain the code.
This was a fun question. Here's my full solution:
ExprEval[nums_, ops_] := Fold[
#2[[1]][#1, #2[[2]]] &,
First#nums,
Transpose[{ops, Rest#nums}]]
SymbolicEval[nums_, ops_] := ExprEval[nums, ToString /# ops]
GetExpression[nums_, ops_, target_] := Select[
Tuples[ops, Length#nums - 1],
(target == ExprEval[nums, #]) &]
Usage example:
nums = {-1, 1, 2, 3};
ops = {Plus, Subtract, Times, Divide};
solutions = GetExpression[nums, ops, 3]
ExprEval[nums, #] & /# solutions
SymbolicEval[nums, #] & /# solutions
Outputs:
{{Plus, Times, Plus}, {Plus, Divide, Plus}, {Subtract, Plus,
Plus}, {Times, Plus, Times}, {Divide, Plus, Times}}
{3, 3, 3, 3, 3}
{"Plus"["Times"["Plus"[-1, 1], 2], 3],
"Plus"["Divide"["Plus"[-1, 1], 2], 3],
"Plus"["Plus"["Subtract"[-1, 1], 2], 3],
"Times"["Plus"["Times"[-1, 1], 2], 3],
"Times"["Plus"["Divide"[-1, 1], 2], 3]}
How it works
The ExprEval function takes in the numbers and operations, and applies them using (I think) RPN:
ExprEval[{1, 2, 3}, {Plus, Times}] == (1 + 2) * 3
It does this by continually folding pairs of numbers using the appropriate operation.
Now that I have a way to evaluate an expression tree, I just needed to generate them. Using Tuples, I'm able to generate all the different operators that I would intersperse between the numbers.
Once you get all possible operations, I used Select to pick out the the ones that evaluate to the target number.
Drawbacks
The solution above is really slow. Generating all the possible tuples is exponential in time. If there are k operations and n numbers, it's on the order of O(k^n).
For n = 10, it took 6 seconds to complete on Win 7 x64, Core i7 860, 12 GB RAM. The timings of the runs match the theoretical time complexity almost exactly:
Red line is the theoretical, blue is experimental. The x-axis is size of the nums input and the y-axis is the time in seconds to enumerate all solutions.
The above solution also solves the problem using a functional programming style. It looks pretty, but the thing also sucks up a butt ton of memory since it's storing the full results at nearly every step.
It doesn't even make use of parallelization, and I'm not entirely certain how you would even parallelize the solution I produced.
Some limitations
Mr. Wizard brought to my attention that this code only solves for only particular set of solutions. Given some input such as {a, b, c, d, e, ... } it only permutes the operators in between the numbers. It doesn't permute the ordering of the numbers. If it were to permute the numbers as well, the time complexity would rise up to O(k^n * n!) where k is the number of operators and n is the length of the input number array.
The following will produce the set of solutions for any permutation of the input numbers and operators:
(* generates a lists of the form
{
{number permutation, {{op order 1}, {op order 2}, ... }
}, ...
}*)
GetAllExpressions[nums_, ops_, target_] :=
ParallelMap[{#, GetExpression[#, ops, target]} &,
Tuples[nums, Length#nums]]
I'm looking for a way to make the best possible combination of people in groups. Let me sketch the situation.
Say we have persons A, B, C and D. Furthermore we have groups 1, 2, 3, 4 and 5. Both are examples and can be less or more. Each person gives a rating to each other person. So for example A rates B a 3, C a 2, and so on. Each person also rates each group. (Say ratings are 0-5). Now I need some sort of algorithm to distribute these people evenly over the groups while keeping them as happy as possible (as in: They should be in a highrated group, with highrated people). Now I know it's not possible for the people to be in the best group (the one they rated a 5) but I need them to be in the best possible solution for the entire group.
I think this is a difficult question, and I would be happy if someone could direct me to some more information about this types of problems, or help me with the algo I'm looking for.
Thanks!
EDIT:
I see a lot of great answers but this problem is too great for me too solve correctly. However, the answers posted so far give me a great starting point too look further into the subject. Thanks a lot already!
after establishing this is NP-Hard problem, I would suggest as a heuristical solution: Artificial Intelligence tools.
A possible approach is steepest ascent hill climbing [SAHC]
first, we will define our utility function (let it be u). It can be the sum of total happiness in all groups.
next,we define our 'world': S is the group of all possible partitions.
for each legal partition s of S, we define:
next(s)={all possibilities moving one person to a different group}
all we have to do now is run SAHC with random restarts:
1. best<- -INFINITY
2. while there is more time
3. choose a random partition as starting point, denote it as s.
4. NEXT <- next(s)
5. if max{ U(NEXT) } < u(s): //s is the top of the hill
5.1. if u(s) > best: best <- u(s) //if s is better then the previous result - store it.
5.2. go to 2. //restart the hill climbing from a different random point.
6. else:
6.1. s <- max{ NEXT }
6.2. goto 4.
7. return best //when out of time, return the best solution found so far.
It is anytime algorithm, meaning it will get a better result as you give it more time to run, and eventually [at time infinity] it will find the optimal result.
The problem is NP-hard: you can reduce from Maximum Triangle Packing, that is, finding at least k vertex-disjoint triangles in a graph, to the version where there are k groups of size 3, no one cares about which group he is in, and likes everyone for 0 or for 1. So even this very special case is hard.
To solve it, I would try using an ILP: have binary variables g_ik indicating that person i is in group k, with constraints to ensure a person is only in one group and a group has an appropriate size. Further, binary variables t_ijk that indicate that persons i and j are together in group k (ensured by t_ijk <= 0.5 g_ik + 0.5 g_jk) and binary variables t_ij that indicate that i and j are together in any group (ensured by t_ij <= sum_k t_ijk). You can then maximize the happiness function under these constraints.
This ILP has very many variables, but modern solvers are pretty good and this approach is very easy to implement.
This is an example of an optimization problem. It is a very well
studied type of problems with very good methods to solve them. Read
Programming Collective Intelligence which explains it much better
than me.
Basically, there are three parts to any kind of optimization problem.
The input to the problem solving function.
The solution outputted by the problem solving function.
A scoring function that evaluates how optimal the solution is by
scoring it.
Now the problem can be stated as finding the solution that produces
the highest score. To do that, you first need to come up with a format
to represent a possible solution that the scoring function can then
score. Assuming 6 persons (0-5) and 3 groups (0-2), this python data structure
would work and would be a possible solution:
output = [
[0, 1],
[2, 3],
[4, 5]
]
Person 0 and 1 is put in group 0, person 2 and 3 in group 1 and so
on. To score this solution, we need to know the input and the rules for
calculating the output. The input could be represented by this data
structure:
input = [
[0, 4, 1, 3, 4, 1, 3, 1, 3],
[5, 0, 1, 2, 1, 5, 5, 2, 4],
[4, 1, 0, 1, 3, 2, 1, 1, 1],
[2, 4, 1, 0, 5, 4, 2, 3, 4],
[5, 5, 5, 5, 0, 5, 5, 5, 5],
[1, 2, 1, 4, 3, 0, 4, 5, 1]
]
Each list in the list represents the rating the person gave. For
example, in the first row, the person 0 gave rating 0 to person 0 (you
can't rate yourself), 4 to person 1, 1 to person 2, 3 to 3, 4 to 4 and
1 to person 5. Then he or she rated the groups 0-2 3, 1 and 3
respectively.
So above is an example of a valid solution to the given input. How do
we score it? That's not specified in the question, only that the
"best" combination is desired therefore I'll arbitrarily decide that
the score for a solution is the sum of each persons happiness. Each
persons happiness is determined by adding his or her rating of the
group with the average of the rating for each person in the group,
excluding the person itself.
Here is the scoring function:
N_GROUPS = 3
N_PERSONS = 6
def score_solution(input, output):
tot_score = 0
for person, ratings in enumerate(input):
# Check what group the person is a member of.
for group, members in enumerate(output):
if person in members:
# Check what rating person gave the group.
group_rating = ratings[N_PERSONS + group]
# Check what rating the person gave the others.
others = list(members)
others.remove(person)
if not others:
# protect against zero division
person_rating = 0
else:
person_ratings = [ratings[o] for o in others]
person_rating = sum(person_ratings) / float(len(person_ratings))
tot_score += group_rating + person_rating
return tot_score
It should return a score of 37.0 for the given solution. Now what
we'll do is to generate valid outputs while keeping track of which one
is best until we are satisfied:
from random import choice
def gen_solution():
groups = [[] for x in range(N_GROUPS)]
for person in range(N_PERSONS):
choice(groups).append(person)
return groups
# Generate 10000 solutions
solutions = [gen_solution() for x in range(10000)]
# Score them
solutions = [(score_solution(input, sol), sol) for sol in solutions]
# Sort by score, take the best.
best_score, best_solution = sorted(solutions)[-1]
print 'The best solution is %s with score %.2f' % (best_solution, best_score)
Running this on my computer produces:
The best solution is [[0, 1], [3, 5], [2, 4]] with score 47.00
Obviously, you may think it is a really stupid idea to randomly just
generate solutions to throw at the problem, and it is. There are much
more sophisticated methods to generate solutions such as simulated
annealing or genetic optimization. But they all build upon the same
framework as given above.