A Note on the Erdös Distinct Subset Sums Problem

2021 ◽  
Vol 35 (1) ◽  
pp. 322-324
Author(s):  
Quentin Dubroff ◽  
Jacob Fox ◽  
Max Wenqiang Xu
Keyword(s):  
1988 ◽  
Vol 50 (181) ◽  
pp. 297-297 ◽  
Author(s):  
W. F. Lunnon
Keyword(s):  

10.37236/1341 ◽  
1997 ◽  
Vol 5 (1) ◽  
Author(s):  
Tom Bohman

A set S of positive integers has distinct subset sums if there are $2^{|S|}$ distinct elements of the set $\left\{ \sum_{x \in X} x: X \subset S \right\} . $ Let $$f(n) = \min\{ \max S: |S|=n {\rm \hskip2mm and \hskip2mm} S {\rm \hskip2mm has \hskip2mm distinct \hskip2mm subset \hskip2mm sums}\}.$$ Erdős conjectured $ f(n) \ge c2^{n}$ for some constant c. We give a construction that yields $f(n) < 0.22002 \cdot 2^{n}$ for n sufficiently large. This now stands as the best known upper bound on $ f(n).$


1993 ◽  
Vol 63 (2) ◽  
pp. 234-256
Author(s):  
Ernest Brickell ◽  
Michael Saks

Author(s):  
SÁNDOR Z. KISS ◽  
VINH HUNG NGUYEN

Abstract Let k and l be positive integers satisfying $k \ge 2, l \ge 1$ . A set $\mathcal {A}$ of positive integers is an asymptotic basis of order k if every large enough positive integer can be represented as the sum of k terms from $\mathcal {A}$ . About 35 years ago, P. Erdős asked: does there exist an asymptotic basis of order k where all the subset sums with at most l terms are pairwise distinct with the exception of a finite number of cases as long as $l \le k - 1$ ? We use probabilistic tools to prove the existence of an asymptotic basis of order $2k+1$ for which all the sums of at most k elements are pairwise distinct except for ‘small’ numbers.


2008 ◽  
Vol 132 (7) ◽  
pp. 1055-1061 ◽  
Author(s):  
Teri J. Franks ◽  
Jeffrey R. Galvin

Abstract Context.—Tumors with neuroendocrine morphology are a distinct subset of lung neoplasms sharing characteristic histologic, immunohistochemical, ultrastructural, and molecular features. Objective.—To review the current histologic classification and the diagnostic criteria for the major categories of neuroendocrine tumors of the lung. Data Sources.—Published classification systems from the World Health Organization and pertinent peer-reviewed articles indexed in PubMed (National Library of Medicine) form the basis of this review. Conclusions.—Accurate classification of the neuroendocrine tumors of the lung requires knowledge of specific criteria separating the major categories, which is essential for determining prognosis and treatment.


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