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Ce Jin

Tsinghua University

22 papers hereh-index 15463 citations27 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author4
  • first author7
  • middle author6
  • last author4

Across the 21 of 22 papers where every author was matched, so the position is known.

fields
  • cs.DS19
  • cs.CC2
  • cs.DC1
affiliations
  • Tsinghua University
Homepage
same name
  • Ce Jin — 8 papers, h 2
  • Ce Jin — 6 papers, h 3
  • Ce Jin — 4 papers, h 1
  • Ce Jin — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182024
most citedA Simple Near-Linear Pseudopolynomial Time Randomized Algorithm for Subset Sum

15 citations · 58 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.DS2023★ 1 cited

Solving Knapsack with Small Items via L0-Proximity

Ce Jin

We study pseudo-polynomial time algorithms for the fundamental \emph{0-1 Knapsack} problem. In terms of n and wmax​, previous algorithms for 0-1 Knapsack have cubic time com…

cs.DS2023★ 3 cited

0-1 Knapsack in Nearly Quadratic Time

Ce Jin

We study pseudo-polynomial time algorithms for the fundamental \emph{0-1 Knapsack} problem. Recent research interest has focused on its fine-grained complexity with respect to the…

cs.DS2023

An Efficient Algorithm for All-Pairs Bounded Edge Connectivity

Shyan Akmal, Ce Jin

Our work concerns algorithms for an unweighted variant of Maximum Flow. In the All-Pairs Connectivity (APC) problem, we are given a graph G on n vertices and m edges, and are…

cs.DS2023★ 10 cited

Approximating Knapsack and Partition via Dense Subset Sums

Mingyang Deng, Ce Jin, Xiao Mao

Knapsack and Partition are two important additive problems whose fine-grained complexities in the (1−ε)-approximation setting are not yet settled. In this work, we make…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.