A fast approximation algorithm for the subset-sum problem - Gens, Levner - 1994G. Gens and E. Levner. A fast approximation algorithm for the subset-sum problem INFOR, 32, pages 143-148, 1994.G.V. Gens, E.V. Levner, A fast approximation algorithm for the subset-sum problem, INFOR ...
Solving subset sum problem by backtracking and Approximation approach cbacktracking-algorithmsubset-sumapproximation-algorithmsdsa-algorithm UpdatedMar 12, 2020 C ToWear is an application that suggests outfits to users from their closets based on the weather. We use linear regression to personalize the ...
The Multiple Subset Sum Problem (MSSP) is the variant of bin packing in which the number of bins is given and one would like to maximize the overall weight
Computing all subset sums for some u also answers the standard subset sum problem with any target value less than or equal to u. 2 Our main contribution is a new algorithm for computing the all subset sums problem, and consequently for the subset sum problem, in O˜(min{ nu, u5/4, ...
As one of Karp’s 21 NP-complete problems, the subset sum problem, as well as its generalization, has been well studied. Among the rich literature, th
The problem is NP-hard in the strong sense, as it can be proved through the same reduction mentioned for the MKP. A PTAS for the MSSP was presented by Caprara et al. (2000a), who also described a 23-approximation algorithm for the bottleneck version of the problem (where the minimum ...
There are a number of approximation algorithms for NP-hard versions of low rank approximation, such as finding a rank-k matrix B minimizing the sum of absolute values of differences to a given n-by-n matrix A, minrank-k B∥A−B∥1...
Second, we give a greedy approximation algorithm for the densest subgraph with a specified subset problem. We show that the greedy algorithm is of approximation ratio $2\\cdot (1+ \\frac{k}{3})$, where $k$ is the element number of the specified subset. 展开 ...
(2022) introduced the multitrend conditional value at risk (MT-CVaR) algorithm, which incorporates an approximation of CVaR as a regularization term in its objective. However, this regularization approach may lead to the generation of non-sparse portfolios in certain real-world financial markets. ...
The new data sketch introduced here is sometimes referred to as Unbiased Space-Saving. This data sketch simultaneously addresses two common data analysis problems: the disaggregated subset sum problem and the frequent item problem. This makes the sketch more flexible than previous sketches, which addre...