I want to have x vehicle which goes on a graph going from vertex 1 and ending in the same all vertices as to be seen one time by one and only one vehicle (for those who know, i'm interesting in a PDPTW problem, but i'm stuck at this point)
using JuMP
using Cbc
nbVertex = 5
nbTransp = 2
model = Model(optimizer_with_attributes(Cbc.Optimizer, "seconds" => limiteExec))
set_optimizer_attribute(model, "PrimalTolerance", 1e-9)
print(model)
#les variables de decisions
#variable(model, route[1:nbVertex,1:nbVertex,1:nbTransp],Bin) #if road between 2 vertex is taken by vehic v
#variable(model, object>=0)
#constraint(model, [v in 1:nbTransp],sum(route[1,i,v] for i in 1:nbVertex)==1)#starting at one
#constraint(model, [v in 1:nbTransp],sum(route[i,1,v] for i in 1:nbVertex)==1)#ending at one
#constraint(model, [j=2: nbVertex],sum(route[i,j,v] for i in 1:nbVertex , v in 1 : nbTransp if i != j )==1)
# all vertices as to be seen by one and only one vehicule
#constraint(model, [j=1:nbVertex, v= 1: nbTransp],sum(route[i,j,v] for i in 1:nbVertex if i != j)-sum(route[j,k,v] for k in 1:nbVertex if k != j)==0)
# here is the constraint
#objective(model, Min,object)
#show model
optimize!(model)
for k in 1:nbTransp
dataTmp=Matrix(undef,2,0)
for i in 1:nbVertex
for j in 1:nbVertex
if value.(route[i,j,k])==1
dataTmp=hcat(dataTmp,[i,j])
println("vehicule ", k, " from ", i, " to ", j, ": $(route[i,j,k]) " )
end
end
end
end
vehicule 1 from 1 to 2: route[1,2,1]
vehicule 1 from 2 to 1: route[2,1,1]
vehicule 2 from 1 to 3: route[1,3,2]
vehicule 2 from 3 to 1: route[3,1,2]
vehicule 2 from 4 to 5: route[4,5,2]
vehicule 2 from 5 to 4: route[5,4,2]
why is vehicle 2 looping in 4->5->4->5->4 ...?
You need to add constraint forbidding cycles in the graph.
If you represent your route as x and the set of vertices as N (that is N = 1:nbVertex it can be denoted as:
This makes sure that for any given sub-set of vertices you will have less travels than the number of vertices.
In practice this constraint will look something like this:
using Combinatorics
N = 1:nbVertex
for E in powerset(N,2,nbVertex)
#constraint(mo, [k in K], ∑(route[i,j,k] for i ∈ E, j ∈ E if i != j) <= length(E)-1)
end
The problem is that the number of possible cycles grows very quickly when the size of N increases. You could try to mitigate it by using a lazy constraints callbacks approach (there is lots of literature on that) - unfortunately it is available only with commercial solvers. GLPK supports lazy constraints but the last time I tested it it was buggy. CBC has no support for lazy constraints.
Related
My code is as follows:
gekko = GEKKO(remote=True)
# create variable, each variable is a vector, each element
# of the vector is a binary
s = []
for i in range(N):
s.append(gekko.Array(gekko.Var, s_len[i], value=0, lb=0, ub=1, integer=True))
# some constants used in the objective/constraint function
c, d, r, m, L = create_c_d_r_m_L() # they are all numpy ndarry
# define the objective function
def objective():
obj = 0
for i in range(N):
obj += np.dot(s[i], c[i]) + np.dot(s[i], d[i])
for idx, (i, j) in enumerate(E):
obj += np.dot(np.dot(s[i], r[idx].reshape(s_len[i], s_len[j])),\
s[j]) # s[i] * r[i, j] * s[j]
return obj
# add constraints
# (a) each vector can only have and must have one 1
for i in range(N):
gekko.Equation(gekko.sum(s[i]) == 1)
# (b)
for t in range(N):
peak_mem = gekko.sum([np.dot(s[i], m[i]) for i in L[t]])
gekko.Equation(peak_mem < DEVICE_MEM)
# DEVICE_MEM is a predefined big int
# solve
gekko.Obj(objective())
gekko.solve(disp=True)
I found that when removing constraint (b), the solver can output the optimal solution for s. However, if we add (b) and set DEVICE_MEM to a very large number (which should not affect the solution), the s is not optimal anymore. I'm wondering if I am doing something wrong here because I tried both APOPT(solvertype=1) and IPOPT (solvertype=3) and they give the same nonoptimal results.
To give more context to the problem: this is an optimization over the graph. N represents the number of nodes in the graph. E is the set that contains all edges in the graph. c, d, m are three types of cost of a node. r is the cost of edges. Each node has multiple strategies (represented by the vector s[i]), and we need to select the best strategy for each node so that the overall cost is minimal.
Detailed constants:
# s_len: record the length of each vector
# (the # of strategies for each node,
# here we assume the length are all 10)
s_len = np.ones(N) * 10
# c, d, m are the costs of each node
# let's assume the c/d/m cost for i node is just i
c, d, m = [], [], []
for i in range(N):
c[i] = s_len[i] * [i]
d[i] = s_len[i] * [i]
m[i] = s_len[i] * [i]
# r is the edge cost, let's assume the cost for
# each edge is just i * j
r = []
for (i,j) in E: # E records all edges
cur_r = s_len[i] * s_len[j] * [i*j]
r.append(cur_r)
# L contains the node ids, we just randomly generate 10 integers here
L = []
for i in range(N):
cur_L = [randrange(N) for _ in range(10)]
L.append(cur_L)
I've been stuck on this for a while and any comments/answers are highly appreciated! Thanks!
Try reframing the inequality constraint:
for t in range(N):
peak_mem = gekko.sum([np.dot(s[i], m[i]) for i in L[t]])
gekko.Equation(peak_mem < DEVICE_MEM)
as a variable with an upper bound:
peak_mem = m.Array(m.Var,N,ub=DEVICE_MEM)
for t in range(N):
m.Equation(peak_mem[t]==\
gekko.sum([np.dot(s[i], m[i]) for i in L[t]])
The N inequality constraints peak_mem < DEVICE_MEM are converted to equality constraints with slack variables as s[i] = DEVICE_MEM - peak_mem with a simple inequality constraint on the slack s[i]>=0. If the inequality constraint far from the bound, then the slack variable can be very large. Formulating the equation as a variable may help.
I tried using the information in the question to pose a minimal problem that could reproduce the error and the potential solution. If you need more specific suggestions, please modify the code to be a complete and minimal example that reproduces the error. This helps with verifying the solution.
I am new to Linear Algebra and learning about triangular systems implemented in Julia lang. I have a col_bs() function I will show here that I need to do a mathematical flop count of. It doesn't have to be super technical this is for learning purposes. I tried to break the function down into it's inner i loop and outer j loop. In between is a count of each FLOP , which I assume is useless since the constants are usually dropped anyway.
I also know the answer should be N^2 since its a reversed version of the forward substitution algorithm which is N^2 flops. I tried my best to derive this N^2 count but when I tried I ended up with a weird Nj count. I will try to provide all work I have done! Thank you to anyone who helps.
function col_bs(U, b)
n = length(b)
x = copy(b)
for j = n:-1:2
if U[j,j] == 0
error("Error: Matrix U is singular.")
end
x[j] = x[j]/U[j,j]
for i=1:j-1
x[i] = x[i] - x[j] * U[i , j ]
end
end
x[1] = x[1]/U[1,1]
return x
end
1: To start 2 flops for the addition and multiplication x[i] - x[j] * U[i , j ]
The $i$ loop does: $$ \sum_{i=1}^{j-1} 2$$
2: 1 flop for the division $$ x[j] / = U[j,j] $$
3: Inside the for $j$ loop in total does: $$ 1 + \sum_{i=1}^{j-1} 2$$
4:The $j$ loop itself does:$$\sum_{j=2}^n ( 1 + \sum_{i=1}^{j-1} 2)) $$
5: Then one final flop for $$ x[1] = x[1]/U[1,1].$$
6: Finally we have
$$\\ 1 + (\sum_{j=2}^n ( 1 + \sum_{i=1}^{j-1} 2))) .$$
Which we can now break down.
If we distribute and simplify
$$\\ 1 + (\sum_{j=2}^n + \sum_{j=2}^n \sum_{i=1}^{j-1} 2) .$$
We can look at only the significant variables and ignore constants,
$$\\
\\ 1 + (n + n(j-1))
\\ n + nj - n
\\ nj
$$
Which then means that if we ignore constants the highest possibility of flops for this formula would be $n$ ( which may be a hint to whats wrong with my function since it should be $n^2$ just like the rest of our triangular systems I believe)
Reduce your code to this form:
for j = n:-1:2
...
for i = 1:j-1
... do k FLOPs
end
end
The inner loop takes k*(j-1) flops. The cost of the outer loop is thus
Since you know that j <= n, you know that this sum is less than (n-1)^2 which is enough for big O.
In fact, however, you should also be able to figure out that
I came across this question in a coding competition. Given a number n, concatenate the binary representation of first n positive integers and return the decimal value of the resultant number formed. Since the answer can be large return answer modulo 10^9+7.
N can be as large as 10^9.
Eg:- n=4. Number formed=11011100(1=1,10=2,11=3,100=4). Decimal value of 11011100=220.
I found a stack overflow answer to this question but the problem is that it only contains a O(n) solution.
Link:- concatenate binary of first N integers and return decimal value
Since n can be up to 10^9 we need to come up with solution that is better than O(n).
Here's some Python code that provides a fast solution; it uses the same ideas as in Abhinav Mathur's post. It requires Python >= 3.8, but it doesn't use anything particularly fancy from Python, and could easily be translated into another language. You'd need to write algorithms for modular exponentiation and modular inverse if they're not already available in the target language.
First, for testing purposes, let's define the slow and obvious version:
# Modulus that results are reduced by,
M = 10 ** 9 + 7
def slow_binary_concat(n):
"""
Concatenate binary representations of 1 through n (inclusive).
Reinterpret the resulting binary string as an integer.
"""
concatenation = "".join(format(k, "b") for k in range(n + 1))
return int(concatenation, 2) % M
Checking that we get the expected result:
>>> slow_binary_concat(4)
220
>>> slow_binary_concat(10)
462911642
Now we'll write a faster version. First, we split the range [1, n) into subintervals such that within each subinterval, all numbers have the same length in binary. For example, the range [1, 10) would be split into four subintervals: [1, 2), [2, 4), [4, 8) and [8, 10). Here's a function to do that splitting:
def split_by_bit_length(n):
"""
Split the numbers in [1, n) by bit-length.
Produces triples (a, b, 2**k). Each triple represents a subinterval
[a, b) of [1, n), with a < b, all of whose elements has bit-length k.
"""
a = 1
while n > a:
b = 2 * a
yield (a, min(n, b), b)
a = b
Example output:
>>> list(split_by_bit_length(10))
[(1, 2, 2), (2, 4, 4), (4, 8, 8), (8, 10, 16)]
Now for each subinterval, the value of the concatenation of all numbers in that subinterval is represented by a fairly simple mathematical sum, which can be computed in exact form. Here's a function to compute that sum modulo M:
def subinterval_concat(a, b, l):
"""
Concatenation of values in [a, b), all of which have the same bit-length k.
l is 2**k.
Equivalently, sum(i * l**(b - 1 - i)) for i in range(a, b)) modulo M.
"""
n = b - a
inv = pow(l - 1, -1, M)
q = (pow(l, n, M) - 1) * inv
return (a * q + (q - n) * inv) % M
I won't go into the evaluation of the sum here: it's a bit off-topic for this site, and it's hard to express without a good way to render formulas. If you want the details, that's a topic for https://math.stackexchange.com, or a page of fairly simple algebra.
Finally, we want to put all the intervals together. Here's a function to do that.
def fast_binary_concat(n):
"""
Fast version of slow_binary_concat.
"""
acc = 0
for a, b, l in split_by_bit_length(n + 1):
acc = (acc * pow(l, b - a, M) + subinterval_concat(a, b, l)) % M
return acc
A comparison with the slow version shows that we get the same results:
>>> fast_binary_concat(4)
220
>>> fast_binary_concat(10)
462911642
But the fast version can easily be evaluated for much larger inputs, where using the slow version would be infeasible:
>>> fast_binary_concat(10**9)
827129560
>>> fast_binary_concat(10**18)
945204784
You just have to note a simple pattern. Taking up your example for n=4, let's gradually build the solution starting from n=1.
1 -> 1 #1
2 -> 2^2(1) + 2 #6
3 -> 2^2[2^2(1)+2] + 3 #27
4 -> 2^3{2^2[2^2(1)+2]+3} + 4 #220
If you expand the coefficients of each term for n=4, you'll get the coefficients as:
1 -> (2^3)*(2^2)*(2^2)
2 -> (2^3)*(2^2)
3 -> (2^3)
4 -> (2^0)
Let the N be total number of bits in the string representation of our required number, and D(x) be the number of bits in x. The coefficients can then be written as
1 -> 2^(N-D(1))
2 -> 2^(N-D(1)-D(2))
3 -> 2^(N-D(1)-D(2)-D(3))
... and so on
Since the value of D(x) will be the same for all x between range (2^t, 2^(t+1)-1) for some given t, you can break the problem into such ranges and solve for each range using mathematics (not iteration). Since the number of such ranges will be log2(Given N), this should work in the given time limit.
As an example, the various ranges become:
1. 1 (D(x) = 1)
2. 2-3 (D(x) = 2)
3. 4-7 (D(x) = 3)
4. 8-15 (D(x) = 4)
I have to implement the Schulze method in SciLab. Unfortunately, I'm completely new to this tool and neither am I good at such kind of job. Could anyone advise something as to where to look for some examples and tools to do it as fast and easy as possible? The program shouldn't be extremely flexible or qualitative, it would be fine if it just worked with some hard-coded input. As I understand, the Schulze method can be implemented using graph and I've found a toolbox for SciLab. Should I use it?
Update:
Here's what I managed to come up with. The code is a total mess and I admit it since I'm really bad at working with such kind of languages. I've tested it with the example from Wikipedia, seems to work.
The code is too long so here's a pastebin
You can find a pseudo implementation on Wikipedia:
# Input: d[i,j], the number of voters who prefer candidate i to candidate j.
# Output: p[i,j], the strength of the strongest path from candidate i to candidate j.
for i from 1 to C
for j from 1 to C
if (i ≠ j) then
if (d[i,j] > d[j,i]) then
p[i,j] := d[i,j]
else
p[i,j] := 0
for i from 1 to C
for j from 1 to C
if (i ≠ j) then
for k from 1 to C
if (i ≠ k and j ≠ k) then
p[j,k] := max ( p[j,k], min ( p[j,i], p[i,k] ) )
Translating this to SciLab would require using functions, for-loops, if-else constructs, max, min.
Below I plainly translated the pseudo code into Scilab code. I haven't tested it and you'll have to find out the arguments to call it with.
function p = schulzeMethod(d, C)
// Initialize a zero matrix p of the same size as d
p = zeros(size(d))
for i = 1:C
for j = 1:C
if i ~= j then
if d(i,j) > d(j,i) then
p(i,j) = d(i,j)
else
p(i,j) = 0
end
end
end
end
for i = 1:C
for j = 1:C
if i ~= j then
for k = 1:C
if (i ~= k) & ( j ~= k) then
p(j,k) = max(p(j,k), min(p(j,i), p(i,k)))
end
end
end
end
end
endfunction
// Create some random values
C = 10
d = rand(C, C)
// Call the function above
p = schulzeMethod(d, C)
disp(p)
Good luck, hope it helps! Please give some feedback if it worked to help others.
so I've been working on a program in Python that finds the minimum weight triangulation of a convex polygon. This means that it finds the weight(The sum of all the triangle perimeters), as well as the list of chords(lines going through the polygon that break it up into triangles, not the boundaries).
I was under the impression that I'm using the dynamic programming algorithm, however when I tried using a somewhat more complex polygon it takes forever(I'm not sure how long it takes because I haven't gotten it to finish).
It works fine with a 10 sided polygon, however I'm trying 25 and that's what is making it stall. My teacher gave me the polygons so I assume that the 25 one is supposed to work as well.
Since this algorithm is supposed to be O(n^3), the 25 sided polygon should take roughly 15.625 times longer to calculate, however it's taking way longer seeing that the 10 sided seems instantaneous.
Am I doing some sort of n operation in there that I'm not realizing? I can't see anything I'm doing, except maybe the last part where I get rid of the duplicates by turning the list into a set, however in my program I put a trace after the decomp before the conversion happens, and it's not even reaching that point.
Here's my code, if you guys need anymore info just please ask. Something in there is making it take longer than O(n^3) and I need to find it so I can trim it out.
#!/usr/bin/python
import math
def cost(v):
ab = math.sqrt(((v[0][0] - v[1][0])**2) + ((v[0][1] - v[1][1])**2))
bc = math.sqrt(((v[1][0] - v[2][0])**2) + ((v[1][1] - v[2][1])**2))
ac = math.sqrt(((v[0][0] - v[2][0])**2) + ((v[0][1] - v[2][1])**2))
return ab + bc + ac
def triang_to_chord(t, n):
if t[1] == t[0] + 1:
# a and b
if t[2] == t[1] + 1:
# single
# b and c
return ((t[0], t[2]), )
elif t[2] == n-1 and t[0] == 0:
# single
# c and a
return ((t[1], t[2]), )
else:
# double
return ((t[0], t[2]), (t[1], t[2]))
elif t[2] == t[1] + 1:
# b and c
if t[0] == 0 and t[2] == n-1:
#single
# c and a
return ((t[0], t[1]), )
else:
#double
return ((t[0], t[1]), (t[0], t[2]))
elif t[0] == 0 and t[2] == n-1:
# c and a
# double
return ((t[0], t[1]), (t[1], t[2]))
else:
# triple
return ((t[0], t[1]), (t[1], t[2]), (t[0], t[2]))
file_name = raw_input("Enter the polygon file name: ").rstrip()
file_obj = open(file_name)
vertices_raw = file_obj.read().split()
file_obj.close()
vertices = []
for i in range(len(vertices_raw)):
if i % 2 == 0:
vertices.append((float(vertices_raw[i]), float(vertices_raw[i+1])))
n = len(vertices)
def decomp(i, j):
if j <= i: return (0, [])
elif j == i+1: return (0, [])
cheap_chord = [float("infinity"), []]
old_cost = cheap_chord[0]
smallest_k = None
for k in range(i+1, j):
old_cost = cheap_chord[0]
itok = decomp(i, k)
ktoj = decomp(k, j)
cheap_chord[0] = min(cheap_chord[0], cost((vertices[i], vertices[j], vertices[k])) + itok[0] + ktoj[0])
if cheap_chord[0] < old_cost:
smallest_k = k
cheap_chord[1] = itok[1] + ktoj[1]
temp_chords = triang_to_chord(sorted((i, j, smallest_k)), n)
for c in temp_chords:
cheap_chord[1].append(c)
return cheap_chord
results = decomp(0, len(vertices) - 1)
chords = set(results[1])
print "Minimum sum of triangle perimeters = ", results[0]
print len(chords), "chords are:"
for c in chords:
print " ", c[0], " ", c[1]
I'll add the polygons I'm using, again the first one is solved right away, while the second one has been running for about 10 minutes so far.
FIRST ONE:
202.1177 93.5606
177.3577 159.5286
138.2164 194.8717
73.9028 189.3758
17.8465 165.4303
2.4919 92.5714
21.9581 45.3453
72.9884 3.1700
133.3893 -0.3667
184.0190 38.2951
SECOND ONE:
397.2494 204.0564
399.0927 245.7974
375.8121 295.3134
340.3170 338.5171
313.5651 369.6730
260.6411 384.6494
208.5188 398.7632
163.0483 394.1319
119.2140 387.0723
76.2607 352.6056
39.8635 319.8147
8.0842 273.5640
-1.4554 226.3238
8.6748 173.7644
20.8444 124.1080
34.3564 87.0327
72.7005 46.8978
117.8008 12.5129
162.9027 5.9481
210.7204 2.7835
266.0091 10.9997
309.2761 27.5857
351.2311 61.9199
377.3673 108.9847
390.0396 148.6748
It looks like you have an issue with the inefficient recurision here.
...
def decomp(i, j):
...
for k in range(i+1, j):
...
itok = decomp(i, k)
ktoj = decomp(k, j)
...
...
You've ran into the same kind of issue as a naive recursive implementation of the Fibonacci Numbers, but the way this algorithm works, it'll probably be much worst on the run time. Assuming that is the only issue with you're algorithm, then you just need to use memorization to ensure that the decomp is only calculated once for each unique input.
The way to spot this issue is to print out the values of i, j and k as the triple (i,j,k). In order to obtain a runtime of O(N^3), you shouldn't see the same exact triple twice. However, the triple (22, 24, 23), appears at least twice (in the 25), and is the first such duplicate. That shows the algorithm is calculating the same thing multiple times, which is inefficient, and is bumping up the performance well past O(N^3). I'll leave figuring out what the algorithms actual performance is to you as an exercise. Assuming there isn't something else wrong with the algorithm the algorithm should eventually stop.