Given a matrix $P\in \mathbb{R}^{n*k}$ (just for ease of notation, no matrix or linear algebra is actually needed; bound to $(0,1)$ if necessary), select one number from each row and compute the sum. Find the choice that the produced sum is closest to a given number $x$.

$$ \arg\min_{1\leq j_1,\cdots,j_n\leq k}{|x - \sum_{i=1}^n{P_{ij_i}}|} $$

An direct algorithm with time complexity $k^n$ is trivial. For $n=2$, I could come up with a $k\log k$ algorithm using binary search. But for $k>2$, all I could think of is to transform the matrix to a $R^{2*k^{n/2}}$ equivalent, then applying algorithm for $k=2$. This could be a cutdown, but the complexity is still exponential.

Could I do better than this? Is there any relevant topic, algorithm or data structure that I'm missing?

  • 3
    $\begingroup$ Is the fact that the values are elements of a matrix even relevant? This seems equivalent to the elements just being in a list. Some relevant search terms are subset sum problem, which is equivalent to a knapsack problem. There are lots of algorithms for these problems. $\endgroup$
    – whpowell96
    Commented Jun 1, 2023 at 3:54
  • $\begingroup$ Sorry for the potential confusion. I just found it easier to express with a matrix. Otherwise, no matrix or linear algebra is involved. $\endgroup$
    – obfish
    Commented Jun 1, 2023 at 4:36
  • $\begingroup$ There might be multiple solutions. $\endgroup$
    – MPIchael
    Commented Jun 1, 2023 at 5:51
  • $\begingroup$ @whpowell96 the matrix is relevant insofar as there's a fixed stride in which selection groups are exclusive. This could technically be represented as a list with definitions for such strides, but that would be awkward. $\endgroup$
    – Reinderien
    Commented Jun 2, 2023 at 23:40
  • 1
    $\begingroup$ @Reinderien Thanks for pointing out. I fixed my notation. A bound is added in case of necessity. $\endgroup$
    – obfish
    Commented Jun 3, 2023 at 0:36

1 Answer 1


A typical - though probably not maximally efficient - approach is mixed-integer linear programming. Use binary assignment variables for your element selection, and minimize the absolute error from your total.

Note that your "closest to a given number" is not well-defined. Here I show the Manhattan norm. If you need another norm (Frobenius, etc.) then you will have to use a different approach than LP.

from time import monotonic

import numpy as np
import scipy.sparse
from numpy.random import default_rng
from scipy.optimize import linprog

n = 20
k = 30
rand = default_rng(seed=0)
P = rand.random((n, k))
x = rand.uniform(low=0.3*n, high=0.7*n)

# Decision variables: nk selection of each cell, binary
# Auxiliary variable: sum error absolute upper bound, continuous
c = np.zeros(n*k + 1)
c[-1] = 1  # minimize sum error

integrality = np.ones(n*k + 1, dtype=int)
integrality[-1] = 0

bounds = np.zeros((n*k + 1, 2))
bounds[:-1, 1] = 1
bounds[-1, 1] = np.inf

A_eq = scipy.sparse.bmat((
    (   # exactly one element selected per row (kronecker)
        scipy.sparse.csr_array((n, 1)),
A_ub = np.block([
    [ P.ravel(), -1],  #  R.assign - x <= sumerr: R.assign - sumerr <= x
    [-P.ravel(), -1],  # -R.assign + x <= sumerr: -R.assign - sumerr <= -x

start = monotonic()
result = linprog(
    c=c, integrality=integrality, bounds=bounds, method='highs',
    A_eq=A_eq, b_eq=np.ones(n),
    A_ub=A_ub, b_ub=(x, -x),
stop = monotonic()
print(stop - start, 's')

assignments = result.x[:-1].reshape((n, k))
xappr = (P * assignments).sum()
# print('P:')
# print(P)
# print('Assignments:')
# print(assignments)
print(xappr, '~', x, ' err:', xappr-x)
2.6089999999385327 s
Optimization terminated successfully. (HiGHS Status 7: Optimal)
6.04941432208226 ~ 6.049414630274225  err: -3.0819196528142356e-07
  • $\begingroup$ Thanks for your answer and effort. But there may be a bit misunderstanding in your statement and code (or my description is not clear enough). The target $x$ is a decimal scalar, and the sum of chosen numbers is also a decimal scalar (see the equation). For two scalars, the closeness is clearly defined. $\endgroup$
    – obfish
    Commented Jun 3, 2023 at 7:42
  • $\begingroup$ @obfish Re. scalars - that's already what this code does. Re. closeness of two scalars: incorrect. There are many norms to choose from, for example least squares which is common in optimization but not expressible in an LP context. $\endgroup$
    – Reinderien
    Commented Jun 4, 2023 at 2:08

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