Can this be solved even faster?
up vote
11
down vote
favorite
So I would like to solve the following set of equation for $m_i$ given a set of ${M_m,N_m}$.
$$
m_1 +m_2 +m_3 +m_4 =M_m \
|m_1| +|m_2| +|m_3| +|m_4| =N_m
$$
All variables are integers.
Also $N_m ge M_m$ and their maximum value can reach up-to 30.
I only need the total number of possible solution not the solutions themselves. So my first trivial attempt was to just use Solve
dimNM1[Nm_, Mm_] :=
Length[(Solve[m1 + m2 + m3 + m4 == Mm &&
Abs[m1] + Abs[m2] + Abs[m3] + Abs[m4] == Nm, {m1, m2, m3, m4}, Integers])]
My second slightly non-trivial attempt is the following:-
dimNM2[Nm_, Mm_] :=
Which[Nm === Mm,
Length[Partition[
Flatten[Permutations /@ IntegerPartitions[Nm, {4}, Range[0, Nm]]],
4]], True,
Module[{res},
res = Partition[
Flatten[Permutations /@ IntegerPartitions[Mm, {4}, Range[-Nm, Nm]]],
4];
Length[
Select[res, (Abs[#[[1]]] + Abs[#[[2]]] + Abs[#[[3]]] +
Abs[#[[4]]]) == Nm &]]]]
The second method is much faster than the first specially for $N_m=M_m$.
But I would like to increase the speed further for $N_mge M_m$ case if possible.
dimNM1[2, 2] // AbsoluteTiming
(*{0.177768, 10}*)
dimNM2[2, 2] // AbsoluteTiming
(*{0.0000899056, 10}*)
So is there any other way to solve these equation faster?
equation-solving performance-tuning
add a comment |
up vote
11
down vote
favorite
So I would like to solve the following set of equation for $m_i$ given a set of ${M_m,N_m}$.
$$
m_1 +m_2 +m_3 +m_4 =M_m \
|m_1| +|m_2| +|m_3| +|m_4| =N_m
$$
All variables are integers.
Also $N_m ge M_m$ and their maximum value can reach up-to 30.
I only need the total number of possible solution not the solutions themselves. So my first trivial attempt was to just use Solve
dimNM1[Nm_, Mm_] :=
Length[(Solve[m1 + m2 + m3 + m4 == Mm &&
Abs[m1] + Abs[m2] + Abs[m3] + Abs[m4] == Nm, {m1, m2, m3, m4}, Integers])]
My second slightly non-trivial attempt is the following:-
dimNM2[Nm_, Mm_] :=
Which[Nm === Mm,
Length[Partition[
Flatten[Permutations /@ IntegerPartitions[Nm, {4}, Range[0, Nm]]],
4]], True,
Module[{res},
res = Partition[
Flatten[Permutations /@ IntegerPartitions[Mm, {4}, Range[-Nm, Nm]]],
4];
Length[
Select[res, (Abs[#[[1]]] + Abs[#[[2]]] + Abs[#[[3]]] +
Abs[#[[4]]]) == Nm &]]]]
The second method is much faster than the first specially for $N_m=M_m$.
But I would like to increase the speed further for $N_mge M_m$ case if possible.
dimNM1[2, 2] // AbsoluteTiming
(*{0.177768, 10}*)
dimNM2[2, 2] // AbsoluteTiming
(*{0.0000899056, 10}*)
So is there any other way to solve these equation faster?
equation-solving performance-tuning
Note thatN
has built-in meanings.
– Αλέξανδρος Ζεγγ
Dec 9 at 12:34
OK I have changed it.
– Hubble07
Dec 9 at 12:46
Nice problem. No need to generate candidates... see my reply.
– ciao
2 days ago
add a comment |
up vote
11
down vote
favorite
up vote
11
down vote
favorite
So I would like to solve the following set of equation for $m_i$ given a set of ${M_m,N_m}$.
$$
m_1 +m_2 +m_3 +m_4 =M_m \
|m_1| +|m_2| +|m_3| +|m_4| =N_m
$$
All variables are integers.
Also $N_m ge M_m$ and their maximum value can reach up-to 30.
I only need the total number of possible solution not the solutions themselves. So my first trivial attempt was to just use Solve
dimNM1[Nm_, Mm_] :=
Length[(Solve[m1 + m2 + m3 + m4 == Mm &&
Abs[m1] + Abs[m2] + Abs[m3] + Abs[m4] == Nm, {m1, m2, m3, m4}, Integers])]
My second slightly non-trivial attempt is the following:-
dimNM2[Nm_, Mm_] :=
Which[Nm === Mm,
Length[Partition[
Flatten[Permutations /@ IntegerPartitions[Nm, {4}, Range[0, Nm]]],
4]], True,
Module[{res},
res = Partition[
Flatten[Permutations /@ IntegerPartitions[Mm, {4}, Range[-Nm, Nm]]],
4];
Length[
Select[res, (Abs[#[[1]]] + Abs[#[[2]]] + Abs[#[[3]]] +
Abs[#[[4]]]) == Nm &]]]]
The second method is much faster than the first specially for $N_m=M_m$.
But I would like to increase the speed further for $N_mge M_m$ case if possible.
dimNM1[2, 2] // AbsoluteTiming
(*{0.177768, 10}*)
dimNM2[2, 2] // AbsoluteTiming
(*{0.0000899056, 10}*)
So is there any other way to solve these equation faster?
equation-solving performance-tuning
So I would like to solve the following set of equation for $m_i$ given a set of ${M_m,N_m}$.
$$
m_1 +m_2 +m_3 +m_4 =M_m \
|m_1| +|m_2| +|m_3| +|m_4| =N_m
$$
All variables are integers.
Also $N_m ge M_m$ and their maximum value can reach up-to 30.
I only need the total number of possible solution not the solutions themselves. So my first trivial attempt was to just use Solve
dimNM1[Nm_, Mm_] :=
Length[(Solve[m1 + m2 + m3 + m4 == Mm &&
Abs[m1] + Abs[m2] + Abs[m3] + Abs[m4] == Nm, {m1, m2, m3, m4}, Integers])]
My second slightly non-trivial attempt is the following:-
dimNM2[Nm_, Mm_] :=
Which[Nm === Mm,
Length[Partition[
Flatten[Permutations /@ IntegerPartitions[Nm, {4}, Range[0, Nm]]],
4]], True,
Module[{res},
res = Partition[
Flatten[Permutations /@ IntegerPartitions[Mm, {4}, Range[-Nm, Nm]]],
4];
Length[
Select[res, (Abs[#[[1]]] + Abs[#[[2]]] + Abs[#[[3]]] +
Abs[#[[4]]]) == Nm &]]]]
The second method is much faster than the first specially for $N_m=M_m$.
But I would like to increase the speed further for $N_mge M_m$ case if possible.
dimNM1[2, 2] // AbsoluteTiming
(*{0.177768, 10}*)
dimNM2[2, 2] // AbsoluteTiming
(*{0.0000899056, 10}*)
So is there any other way to solve these equation faster?
equation-solving performance-tuning
equation-solving performance-tuning
edited Dec 9 at 13:00
Henrik Schumacher
47.2k466134
47.2k466134
asked Dec 9 at 11:40
Hubble07
2,935719
2,935719
Note thatN
has built-in meanings.
– Αλέξανδρος Ζεγγ
Dec 9 at 12:34
OK I have changed it.
– Hubble07
Dec 9 at 12:46
Nice problem. No need to generate candidates... see my reply.
– ciao
2 days ago
add a comment |
Note thatN
has built-in meanings.
– Αλέξανδρος Ζεγγ
Dec 9 at 12:34
OK I have changed it.
– Hubble07
Dec 9 at 12:46
Nice problem. No need to generate candidates... see my reply.
– ciao
2 days ago
Note that
N
has built-in meanings.– Αλέξανδρος Ζεγγ
Dec 9 at 12:34
Note that
N
has built-in meanings.– Αλέξανδρος Ζεγγ
Dec 9 at 12:34
OK I have changed it.
– Hubble07
Dec 9 at 12:46
OK I have changed it.
– Hubble07
Dec 9 at 12:46
Nice problem. No need to generate candidates... see my reply.
– ciao
2 days ago
Nice problem. No need to generate candidates... see my reply.
– ciao
2 days ago
add a comment |
3 Answers
3
active
oldest
votes
up vote
9
down vote
accepted
ClearAll[num];
num[n_, m_] /; OddQ[n + m] = 0;
num[n_, n_] := Binomial[n + 3, 3];
num[n_, m_] /; OddQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 3)/2 + 2 z - (2 z^2)];
num[n_, m_] /; EvenQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 4)/2 - (2 z^2)];
Testing vs fastest answer here at writing (Henrik Schumacher):
stop = 100;
res = Table[{n, m, dimNM3[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res2 = Table[{n, m, num[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res == res2
169.203
0.0219434
True
Large cases are a non-issue:
num[123423456, 123412348] // AbsoluteTiming
{0.0000247977, 30468069908023290}
Some quick timings:
3
Pretty impressive. Would you mind to elaborate where these formulas come from or at least to provide an (accessible) source?
– Henrik Schumacher
2 days ago
3
@HenrikSchumacher - I derived them, looking at a set of results: I recognized the pattern(s). Neat that the tetrahedral numbers and coordination sequences popped out. See e.g. Sloan, "Low-Dimensional Lattices VII: Coordination Sequences".
– ciao
2 days ago
2
Chapeaux for recognizing the patterns! =D
– Henrik Schumacher
2 days ago
2
@ciao - You Sir are a genius. Thank you.
– Hubble07
2 days ago
1
Answers from ciao are generally great reads, +1.
– Marius Ladegård Meyer
yesterday
add a comment |
up vote
12
down vote
It is more efficient to first pick the integer partitions whose absolute values sum up to n
before generating the permutations.
dimNM3[n_, m_] := Total[
Map[
Length@*Permutations,
Pick[#, Abs[#].ConstantArray[1, 4], n] &[
IntegerPartitions[m, {4}, Range[-n, n]
]
]
]
];
m = 20;
n = 40;
dimNM1[n, m] // AbsoluteTiming
dimNM2[n, m] // AbsoluteTiming
dimNM3[n, m] // AbsoluteTiming
{0.116977, 3802}
{0.995365, 3802}
{0.005579, 3802}
add a comment |
up vote
2
down vote
Sorry for not knowing much Mathematica, but I have a Python solution you might be able to follow. I'm putting this on the community wiki for anyone who wants to translate it.
def count_solutions(Nm, Mm):
firsthalves = dict()
for m1 in range(-Nm,Nm+1):
for m2 in range(-Nm,Nm+1):
m = m1+m2
n = abs(m1)+abs(m2)
key = (m,n)
if key in firsthalves:
firsthalves[key] += 1
else:
firsthalves[key] = 1
solutions = 0
for m3 in range(-Nm,Nm+1):
for m4 in range(-Nm,Nm+1):
m = m3+m4
n = abs(m3)+abs(m4)
key = (Mm-m, Nm-n)
if key in firsthalves:
solutions += firsthalves[key]
return solutions
This is a meet in the middle strategy. I enumerate all the possible $m1,m2$ combinations and record how many times each $m1+m2,|m1|+|m2|$ combination occurs in a dictionary.
Then I go through all the possible $m3,m4$ combinations and for each combination I calculate the necessary $m1+m2,|m1|+|m2|$ combination to make $Mm,Nm$, and I refer to the dictionary to find out how many $m1,m2$ combinations can make that.
The difference is that you go through the $m1,m2$ combination then the $m3,m4$ combinations, and the number of operations is roughly a square root of going through every $m1,m2,m3,m4$ combination. You should be able to solve for $Nm = 1000,Mn = 0$ in a few seconds.
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
9
down vote
accepted
ClearAll[num];
num[n_, m_] /; OddQ[n + m] = 0;
num[n_, n_] := Binomial[n + 3, 3];
num[n_, m_] /; OddQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 3)/2 + 2 z - (2 z^2)];
num[n_, m_] /; EvenQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 4)/2 - (2 z^2)];
Testing vs fastest answer here at writing (Henrik Schumacher):
stop = 100;
res = Table[{n, m, dimNM3[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res2 = Table[{n, m, num[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res == res2
169.203
0.0219434
True
Large cases are a non-issue:
num[123423456, 123412348] // AbsoluteTiming
{0.0000247977, 30468069908023290}
Some quick timings:
3
Pretty impressive. Would you mind to elaborate where these formulas come from or at least to provide an (accessible) source?
– Henrik Schumacher
2 days ago
3
@HenrikSchumacher - I derived them, looking at a set of results: I recognized the pattern(s). Neat that the tetrahedral numbers and coordination sequences popped out. See e.g. Sloan, "Low-Dimensional Lattices VII: Coordination Sequences".
– ciao
2 days ago
2
Chapeaux for recognizing the patterns! =D
– Henrik Schumacher
2 days ago
2
@ciao - You Sir are a genius. Thank you.
– Hubble07
2 days ago
1
Answers from ciao are generally great reads, +1.
– Marius Ladegård Meyer
yesterday
add a comment |
up vote
9
down vote
accepted
ClearAll[num];
num[n_, m_] /; OddQ[n + m] = 0;
num[n_, n_] := Binomial[n + 3, 3];
num[n_, m_] /; OddQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 3)/2 + 2 z - (2 z^2)];
num[n_, m_] /; EvenQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 4)/2 - (2 z^2)];
Testing vs fastest answer here at writing (Henrik Schumacher):
stop = 100;
res = Table[{n, m, dimNM3[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res2 = Table[{n, m, num[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res == res2
169.203
0.0219434
True
Large cases are a non-issue:
num[123423456, 123412348] // AbsoluteTiming
{0.0000247977, 30468069908023290}
Some quick timings:
3
Pretty impressive. Would you mind to elaborate where these formulas come from or at least to provide an (accessible) source?
– Henrik Schumacher
2 days ago
3
@HenrikSchumacher - I derived them, looking at a set of results: I recognized the pattern(s). Neat that the tetrahedral numbers and coordination sequences popped out. See e.g. Sloan, "Low-Dimensional Lattices VII: Coordination Sequences".
– ciao
2 days ago
2
Chapeaux for recognizing the patterns! =D
– Henrik Schumacher
2 days ago
2
@ciao - You Sir are a genius. Thank you.
– Hubble07
2 days ago
1
Answers from ciao are generally great reads, +1.
– Marius Ladegård Meyer
yesterday
add a comment |
up vote
9
down vote
accepted
up vote
9
down vote
accepted
ClearAll[num];
num[n_, m_] /; OddQ[n + m] = 0;
num[n_, n_] := Binomial[n + 3, 3];
num[n_, m_] /; OddQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 3)/2 + 2 z - (2 z^2)];
num[n_, m_] /; EvenQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 4)/2 - (2 z^2)];
Testing vs fastest answer here at writing (Henrik Schumacher):
stop = 100;
res = Table[{n, m, dimNM3[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res2 = Table[{n, m, num[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res == res2
169.203
0.0219434
True
Large cases are a non-issue:
num[123423456, 123412348] // AbsoluteTiming
{0.0000247977, 30468069908023290}
Some quick timings:
ClearAll[num];
num[n_, m_] /; OddQ[n + m] = 0;
num[n_, n_] := Binomial[n + 3, 3];
num[n_, m_] /; OddQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 3)/2 + 2 z - (2 z^2)];
num[n_, m_] /; EvenQ[n] := With[{z = Ceiling[m/2]}, (5*n^2 + 4)/2 - (2 z^2)];
Testing vs fastest answer here at writing (Henrik Schumacher):
stop = 100;
res = Table[{n, m, dimNM3[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res2 = Table[{n, m, num[n, m]}, {n, 1, stop}, {m, 1, n}]; // AbsoluteTiming//First
res == res2
169.203
0.0219434
True
Large cases are a non-issue:
num[123423456, 123412348] // AbsoluteTiming
{0.0000247977, 30468069908023290}
Some quick timings:
edited 2 days ago
answered 2 days ago
ciao
17.1k137106
17.1k137106
3
Pretty impressive. Would you mind to elaborate where these formulas come from or at least to provide an (accessible) source?
– Henrik Schumacher
2 days ago
3
@HenrikSchumacher - I derived them, looking at a set of results: I recognized the pattern(s). Neat that the tetrahedral numbers and coordination sequences popped out. See e.g. Sloan, "Low-Dimensional Lattices VII: Coordination Sequences".
– ciao
2 days ago
2
Chapeaux for recognizing the patterns! =D
– Henrik Schumacher
2 days ago
2
@ciao - You Sir are a genius. Thank you.
– Hubble07
2 days ago
1
Answers from ciao are generally great reads, +1.
– Marius Ladegård Meyer
yesterday
add a comment |
3
Pretty impressive. Would you mind to elaborate where these formulas come from or at least to provide an (accessible) source?
– Henrik Schumacher
2 days ago
3
@HenrikSchumacher - I derived them, looking at a set of results: I recognized the pattern(s). Neat that the tetrahedral numbers and coordination sequences popped out. See e.g. Sloan, "Low-Dimensional Lattices VII: Coordination Sequences".
– ciao
2 days ago
2
Chapeaux for recognizing the patterns! =D
– Henrik Schumacher
2 days ago
2
@ciao - You Sir are a genius. Thank you.
– Hubble07
2 days ago
1
Answers from ciao are generally great reads, +1.
– Marius Ladegård Meyer
yesterday
3
3
Pretty impressive. Would you mind to elaborate where these formulas come from or at least to provide an (accessible) source?
– Henrik Schumacher
2 days ago
Pretty impressive. Would you mind to elaborate where these formulas come from or at least to provide an (accessible) source?
– Henrik Schumacher
2 days ago
3
3
@HenrikSchumacher - I derived them, looking at a set of results: I recognized the pattern(s). Neat that the tetrahedral numbers and coordination sequences popped out. See e.g. Sloan, "Low-Dimensional Lattices VII: Coordination Sequences".
– ciao
2 days ago
@HenrikSchumacher - I derived them, looking at a set of results: I recognized the pattern(s). Neat that the tetrahedral numbers and coordination sequences popped out. See e.g. Sloan, "Low-Dimensional Lattices VII: Coordination Sequences".
– ciao
2 days ago
2
2
Chapeaux for recognizing the patterns! =D
– Henrik Schumacher
2 days ago
Chapeaux for recognizing the patterns! =D
– Henrik Schumacher
2 days ago
2
2
@ciao - You Sir are a genius. Thank you.
– Hubble07
2 days ago
@ciao - You Sir are a genius. Thank you.
– Hubble07
2 days ago
1
1
Answers from ciao are generally great reads, +1.
– Marius Ladegård Meyer
yesterday
Answers from ciao are generally great reads, +1.
– Marius Ladegård Meyer
yesterday
add a comment |
up vote
12
down vote
It is more efficient to first pick the integer partitions whose absolute values sum up to n
before generating the permutations.
dimNM3[n_, m_] := Total[
Map[
Length@*Permutations,
Pick[#, Abs[#].ConstantArray[1, 4], n] &[
IntegerPartitions[m, {4}, Range[-n, n]
]
]
]
];
m = 20;
n = 40;
dimNM1[n, m] // AbsoluteTiming
dimNM2[n, m] // AbsoluteTiming
dimNM3[n, m] // AbsoluteTiming
{0.116977, 3802}
{0.995365, 3802}
{0.005579, 3802}
add a comment |
up vote
12
down vote
It is more efficient to first pick the integer partitions whose absolute values sum up to n
before generating the permutations.
dimNM3[n_, m_] := Total[
Map[
Length@*Permutations,
Pick[#, Abs[#].ConstantArray[1, 4], n] &[
IntegerPartitions[m, {4}, Range[-n, n]
]
]
]
];
m = 20;
n = 40;
dimNM1[n, m] // AbsoluteTiming
dimNM2[n, m] // AbsoluteTiming
dimNM3[n, m] // AbsoluteTiming
{0.116977, 3802}
{0.995365, 3802}
{0.005579, 3802}
add a comment |
up vote
12
down vote
up vote
12
down vote
It is more efficient to first pick the integer partitions whose absolute values sum up to n
before generating the permutations.
dimNM3[n_, m_] := Total[
Map[
Length@*Permutations,
Pick[#, Abs[#].ConstantArray[1, 4], n] &[
IntegerPartitions[m, {4}, Range[-n, n]
]
]
]
];
m = 20;
n = 40;
dimNM1[n, m] // AbsoluteTiming
dimNM2[n, m] // AbsoluteTiming
dimNM3[n, m] // AbsoluteTiming
{0.116977, 3802}
{0.995365, 3802}
{0.005579, 3802}
It is more efficient to first pick the integer partitions whose absolute values sum up to n
before generating the permutations.
dimNM3[n_, m_] := Total[
Map[
Length@*Permutations,
Pick[#, Abs[#].ConstantArray[1, 4], n] &[
IntegerPartitions[m, {4}, Range[-n, n]
]
]
]
];
m = 20;
n = 40;
dimNM1[n, m] // AbsoluteTiming
dimNM2[n, m] // AbsoluteTiming
dimNM3[n, m] // AbsoluteTiming
{0.116977, 3802}
{0.995365, 3802}
{0.005579, 3802}
edited 2 days ago
answered Dec 9 at 13:20
Henrik Schumacher
47.2k466134
47.2k466134
add a comment |
add a comment |
up vote
2
down vote
Sorry for not knowing much Mathematica, but I have a Python solution you might be able to follow. I'm putting this on the community wiki for anyone who wants to translate it.
def count_solutions(Nm, Mm):
firsthalves = dict()
for m1 in range(-Nm,Nm+1):
for m2 in range(-Nm,Nm+1):
m = m1+m2
n = abs(m1)+abs(m2)
key = (m,n)
if key in firsthalves:
firsthalves[key] += 1
else:
firsthalves[key] = 1
solutions = 0
for m3 in range(-Nm,Nm+1):
for m4 in range(-Nm,Nm+1):
m = m3+m4
n = abs(m3)+abs(m4)
key = (Mm-m, Nm-n)
if key in firsthalves:
solutions += firsthalves[key]
return solutions
This is a meet in the middle strategy. I enumerate all the possible $m1,m2$ combinations and record how many times each $m1+m2,|m1|+|m2|$ combination occurs in a dictionary.
Then I go through all the possible $m3,m4$ combinations and for each combination I calculate the necessary $m1+m2,|m1|+|m2|$ combination to make $Mm,Nm$, and I refer to the dictionary to find out how many $m1,m2$ combinations can make that.
The difference is that you go through the $m1,m2$ combination then the $m3,m4$ combinations, and the number of operations is roughly a square root of going through every $m1,m2,m3,m4$ combination. You should be able to solve for $Nm = 1000,Mn = 0$ in a few seconds.
add a comment |
up vote
2
down vote
Sorry for not knowing much Mathematica, but I have a Python solution you might be able to follow. I'm putting this on the community wiki for anyone who wants to translate it.
def count_solutions(Nm, Mm):
firsthalves = dict()
for m1 in range(-Nm,Nm+1):
for m2 in range(-Nm,Nm+1):
m = m1+m2
n = abs(m1)+abs(m2)
key = (m,n)
if key in firsthalves:
firsthalves[key] += 1
else:
firsthalves[key] = 1
solutions = 0
for m3 in range(-Nm,Nm+1):
for m4 in range(-Nm,Nm+1):
m = m3+m4
n = abs(m3)+abs(m4)
key = (Mm-m, Nm-n)
if key in firsthalves:
solutions += firsthalves[key]
return solutions
This is a meet in the middle strategy. I enumerate all the possible $m1,m2$ combinations and record how many times each $m1+m2,|m1|+|m2|$ combination occurs in a dictionary.
Then I go through all the possible $m3,m4$ combinations and for each combination I calculate the necessary $m1+m2,|m1|+|m2|$ combination to make $Mm,Nm$, and I refer to the dictionary to find out how many $m1,m2$ combinations can make that.
The difference is that you go through the $m1,m2$ combination then the $m3,m4$ combinations, and the number of operations is roughly a square root of going through every $m1,m2,m3,m4$ combination. You should be able to solve for $Nm = 1000,Mn = 0$ in a few seconds.
add a comment |
up vote
2
down vote
up vote
2
down vote
Sorry for not knowing much Mathematica, but I have a Python solution you might be able to follow. I'm putting this on the community wiki for anyone who wants to translate it.
def count_solutions(Nm, Mm):
firsthalves = dict()
for m1 in range(-Nm,Nm+1):
for m2 in range(-Nm,Nm+1):
m = m1+m2
n = abs(m1)+abs(m2)
key = (m,n)
if key in firsthalves:
firsthalves[key] += 1
else:
firsthalves[key] = 1
solutions = 0
for m3 in range(-Nm,Nm+1):
for m4 in range(-Nm,Nm+1):
m = m3+m4
n = abs(m3)+abs(m4)
key = (Mm-m, Nm-n)
if key in firsthalves:
solutions += firsthalves[key]
return solutions
This is a meet in the middle strategy. I enumerate all the possible $m1,m2$ combinations and record how many times each $m1+m2,|m1|+|m2|$ combination occurs in a dictionary.
Then I go through all the possible $m3,m4$ combinations and for each combination I calculate the necessary $m1+m2,|m1|+|m2|$ combination to make $Mm,Nm$, and I refer to the dictionary to find out how many $m1,m2$ combinations can make that.
The difference is that you go through the $m1,m2$ combination then the $m3,m4$ combinations, and the number of operations is roughly a square root of going through every $m1,m2,m3,m4$ combination. You should be able to solve for $Nm = 1000,Mn = 0$ in a few seconds.
Sorry for not knowing much Mathematica, but I have a Python solution you might be able to follow. I'm putting this on the community wiki for anyone who wants to translate it.
def count_solutions(Nm, Mm):
firsthalves = dict()
for m1 in range(-Nm,Nm+1):
for m2 in range(-Nm,Nm+1):
m = m1+m2
n = abs(m1)+abs(m2)
key = (m,n)
if key in firsthalves:
firsthalves[key] += 1
else:
firsthalves[key] = 1
solutions = 0
for m3 in range(-Nm,Nm+1):
for m4 in range(-Nm,Nm+1):
m = m3+m4
n = abs(m3)+abs(m4)
key = (Mm-m, Nm-n)
if key in firsthalves:
solutions += firsthalves[key]
return solutions
This is a meet in the middle strategy. I enumerate all the possible $m1,m2$ combinations and record how many times each $m1+m2,|m1|+|m2|$ combination occurs in a dictionary.
Then I go through all the possible $m3,m4$ combinations and for each combination I calculate the necessary $m1+m2,|m1|+|m2|$ combination to make $Mm,Nm$, and I refer to the dictionary to find out how many $m1,m2$ combinations can make that.
The difference is that you go through the $m1,m2$ combination then the $m3,m4$ combinations, and the number of operations is roughly a square root of going through every $m1,m2,m3,m4$ combination. You should be able to solve for $Nm = 1000,Mn = 0$ in a few seconds.
edited Dec 9 at 23:42
community wiki
2 revs
James Hollis
add a comment |
add a comment |
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Note that
N
has built-in meanings.– Αλέξανδρος Ζεγγ
Dec 9 at 12:34
OK I have changed it.
– Hubble07
Dec 9 at 12:46
Nice problem. No need to generate candidates... see my reply.
– ciao
2 days ago