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تسجيل مستخدم جديدNew Algorithms for Subset Sum Problem
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Given a set (or multiset) S of n numbers and a target number t, the subset sum problem is to decide if there is a subset of S that sums up to t. There are several methods for solving this problem, including exhaustive search, divide-and-conquer method, and Bellmans dynamic programming method. However, none of them could generate universal and light code. In this paper, we present a new deterministic algorithm based on a novel data arrangement, which could generate such code and return all solutions. If n is small enough, it is efficient for usual purpose. We also present a probabilistic version with one-sided error and a greedy algorithm which could generate a solution with minimized variance.
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In the Subset Sum problem we are given a set of $n$ positive integers $X$ and a target $t$ and are asked whether some subset of $X$ sums to $t$. Natural parameters for this problem that have been studied in the literature are $n$ and $t$ as well as t
he maximum input number $rm{mx}_X$ and the sum of all input numbers $Sigma_X$. In this paper we study the dense case of Subset Sum, where all these parameters are polynomial in $n$. In this regime, standard pseudo-polynomial algorithms solve Subset Sum in polynomial time $n^{O(1)}$. Our main question is: When can dense Subset Sum be solved in near-linear time $tilde{O}(n)$? We provide an essentially complete dichotomy by designing improved algorithms and proving conditional lower bounds, thereby determining essentially all settings of the parameters $n,t,rm{mx}_X,Sigma_X$ for which dense Subset Sum is in time $tilde{O}(n)$. For notational convenience we assume without loss of generality that $t ge rm{mx}_X$ (as larger numbers can be ignored) and $t le Sigma_X/2$ (using symmetry). Then our dichotomy reads as follows: - By reviving and improving an additive-combinatorics-based approach by Galil and Margalit [SICOMP91], we show that Subset Sum is in near-linear time $tilde{O}(n)$ if $t gg rm{mx}_X Sigma_X/n^2$. - We prove a matching conditional lower bound: If Subset Sum is in near-linear time for any setting with $t ll rm{mx}_X Sigma_X/n^2$, then the Strong Exponential Time Hypothesis and the Strong k-Sum Hypothesis fail. We also generalize our algorithm from sets to multi-sets, albeit with non-matching upper and lower bounds.
We show that Nederlofs algorithm [Information Processing Letters, 118 (2017), 15-16] for constructing a proof that the number of subsets summing to a particular integer equals a claimed quantity is flawed because: 1) its consistence is not kept; 2) the proposed recurrence formula is incorrect.
We revisit the Subset Sum problem over the finite cyclic group $mathbb{Z}_m$ for some given integer $m$. A series of recent works has provided near-optimal algorithms for this problem under the Strong Exponential Time Hypothesis. Koiliaris and Xu (SO
DA17, TALG19) gave a deterministic algorithm running in time $tilde{O}(m^{5/4})$, which was later improved to $O(m log^7 m)$ randomized time by Axiotis et al. (SODA19). In this work, we present two simple algorithms for the Modular Subset Sum problem running in near-linear time in $m$, both efficiently implementing Bellmans iteration over $mathbb{Z}_m$. The first one is a randomized algorithm running in time $O(m log^2 m)$, that is based solely on rolling hash and an elementary data-structure for prefix sums; to illustrate its simplicity we provide a short and efficient implementation of the algorithm in Python. Our second solution is a deterministic algorithm running in time $O(m mathrm{polylog} m)$, that uses dynamic data structures for string manipulation. We further show that the techniques developed in this work can also lead to simple algorithms for the All Pairs Non-Decreasing Paths Problem (APNP) on undirected graphs, matching the near-optimal running time of $tilde{O}(n^2)$ provided in the recent work of Duan et al. (ICALP19).
This paper proves that there does not exist a polynomial-time algorithm to the the subset sum problem. As this problem is in NP, the result implies that the class P of problems admitting polynomial-time algorithms does not equal the class NP of probl
ems admitting nondeterministic polynomial-time algorithms.
We give the first single-pass streaming algorithm for Column Subset Selection with respect to the entrywise $ell_p$-norm with $1 leq p < 2$. We study the $ell_p$ norm loss since it is often considered more robust to noise than the standard Frobenius
norm. Given an input matrix $A in mathbb{R}^{d times n}$ ($n gg d$), our algorithm achieves a multiplicative $k^{frac{1}{p} - frac{1}{2}}text{poly}(log nd)$-approximation to the error with respect to the best possible column subset of size $k$. Furthermore, the space complexity of the streaming algorithm is optimal up to a logarithmic factor. Our streaming algorithm also extends naturally to a 1-round distributed protocol with nearly optimal communication cost. A key ingredient in our algorithms is a reduction to column subset selection in the $ell_{p,2}$-norm, which corresponds to the $p$-norm of the vector of Euclidean norms of each of the columns of $A$. This enables us to leverage strong coreset constructions for the Euclidean norm, which previously had not been applied in this context. We also give the first provable guarantees for greedy column subset selection in the $ell_{1, 2}$ norm, which can be used as an alternative, practical subroutine in our algorithms. Finally, we show that our algorithms give significant practical advantages on real-world data analysis tasks.
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