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Find fast approximate solutions for the subsetsum problem.

Project description

Approximate Subset Sum

Find fast approximate solutions for the subset sum problem.

Installation

pip install approx_subsetsum

Usage

Given an integer numpy array samples, this function tries to find a subset of samples, that has a sum close to a given capacity.

import numpy as np
from approx_subsetsum import subsetsum

samples = np.array([1, 2, 3, 4, 5])
capacity = 7
indices = subsetsum(samples, capacity, timeout=10.0)
solution = samples[indices]
if np.sum(solution) == capacity:
    print('found solution')
else:
    print('solution is off by', abs(np.sum(solution) - capacity))

Performance

The algorithm is not well optimized, but at least implemented in C++.

For the following measurements, a script similar to the following code snippet was executed:

import numpy as np
from approx_subsetsum import subsetsum

def bench(n_samples, capacity):
    samples = np.random.randint(1, 1000000, n_samples)
    subsetsum(samples, capacity, timeout=10.0)

The plot shows the runtime depending on the number of samples and the capacity. As you can see the algorithm is not optimized for large capacities, but scales well with the number of samples. Plot visualizing the runtime Note: The algorithm timed out after 10 seconds.

The following table shows an overview of the runtimes (of course, this depends on the hardware).

#samples capacity runtime
1000 10000 0.000 secs
10000 10000 0.001 secs
100000 10000 0.006 secs
1000000 10000 0.061 secs
1000 100000 0.004 secs
10000 100000 0.057 secs
100000 100000 0.569 secs
1000000 100000 5.887 secs
1000 1000000 0.623 secs
10000 1000000 6.093 secs
100000 1000000 >60 secs
1000000 1000000 >60 secs

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