approx-subsetsum 0.2.2

Find fast approximate solutions for the subsetsum problem.

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, allow_higher=100)
solution = samples[indices]
if np.sum(solution) == capacity:
    print('found solution')
else:
    print('solution is off by', abs(np.sum(solution) - capacity))

Parameters

• samples: The input array of integers. Fastest if given as numpy array (no copy). Otherwise the data will be copied.
• capacity: The target sum as integer. High capacities can lead to high memory usage and runtime.
• timeout: The maximum runtime in seconds. If the algorithm does not find a solution within this time, it throws a approx_subsetsum.TimeoutError.
• allow_higher: If set to a positive integer, the algorithm will also try to find a solution higher than capacity (up to capacity + allow_higher). Note that setting allow_higher to a large value can hurt performance.

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. 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