Breakthrough in All-Pairs Shortest Paths Problem by Computer Scientist

Published
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 RobotToday Industry Briefing RobotToday Industry Briefing graphic: a blueprint-style humanoid robot outline with annotations for perception, actuation and mobility, next to the Industry Briefing wordmark. PERCEPTION ACTUATION MOBILITY ROBOTTODAY Industry Briefing Editor-curated robotics news from around the world HUMANOIDS/ INDUSTRIAL / DRONES / AI & RESEARCH RobotToday robottoday.com/industry-briefing
RobotToday Industry Briefing

A computer scientist has made significant progress in solving the All-Pairs Shortest Paths (APSP) problem, which involves calculating the shortest distance between every pair of points in a network. This challenge is critical for various applications, including transportation systems and communication networks, where determining efficient routes is essential.

The APSP problem has been a longstanding issue since it can consume vast amounts of computing resources, especially in densely connected networks. Traditional methods can lead to exponential increases in computational workload, prompting researchers to seek more efficient solutions. The 1996 method developed by Dor, Halperin, and Zwick (DHZ) provided a 2-approximation, allowing for faster calculations by using sampled vertices.

The DHZ approach excels when comparing points that are far apart, as it can effectively estimate distances within a factor of two of the actual shortest distance. This breakthrough opens new avenues for improving algorithms in various fields, although no further timeline was disclosed at the time of publication.

Original report

A computer scientist breaks through a limit that stood since 1996

ScienceDaily.com
Read at ScienceDaily.com

More related news

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12

Related Suppliers

AB Swiftstrike AI

Swedish defence-tech firm building man-portable autonomous counter-UAS effectors and ISR sensor nodes; integrating AEye STRATOS lidar since 2026.

CACI International Inc

CACI International is a Reston, Virginia defense contractor whose SkyValor counter-drone system won a $500M JIATF-401 contract in 2026.

Acceleration Robotics

Vitoria-Gasteiz, Spain semiconductor startup founded 2021 by Victor Mayoral Vilches; builds FPGA and GPU hardware acceleration for ROS 2 robots.

Perception & Vision

Preferred Networks

Tokyo deep learning company founded 2014; develops MN-Core AI chips, PLaMo LLMs, Kachaka AMR, and physical AI solutions for robotics and manufacturing.

Symage AI (by Geisel Software)

Physics-based synthetic image data platform accelerating AI vision model training for robotics and industrial automation.

Cambrian Inc.

AI 3D-vision system that gives robot arms 6D pose estimation and path planning for bin picking, assembly and cable handling.

Blueflite

U.S. manufacturer of rugged, all-electric tilt-rotor cargo drones with a proprietary cloud logistics platform for commercial last-mile delivery.

NuPort Robotics

Toronto startup retrofitting trucks a fleet already owns with sensors and its NuDrive autonomy stack for middle-mile, yard, mine and forestry hauls.

Techsoft Robots 深圳市泰科智能机器人有限公司

Shenzhen manufacturer of humanoid and collaborative robot joint modules, servo drives, and dual-arm cobots for OEM and end-user customers.

SEQSENSE Inc.

Tokyo startup making the SQ-2 autonomous indoor security patrol robot, deployed in 30+ Japanese facilities including govt buildings and airports.

Mobile Robot (AGV/AMR) Healthcare & Senior Care

Outrider

Colorado maker of battery-electric autonomous yard trucks whose patented robotic arm connects trailer brake and electrical lines without a driver.

RailPod

Boston-based robotic rail inspection platform capturing track geometry, rail wear, catenary data and asset maps in a single pass.

RobotToday Initiative

Robotics needs a service framework.

RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.

inJoin the RobotToday community on LinkedIn

Daily robotics news, in-depth analysis, conference highlights, and discussions with professionals worldwide.