AI Paper Atlas

Research map / Learning theory and algorithms / Submodular and game algorithms

Submodular Optimization with Routing Constraints

AAAI 2016 · Learning theory and algorithms › Submodular and game algorithms · approach: Monotone submodular maximization · 150 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
use generalized cost-benefit greedy (GCB) instead of simple greedy (GR)submodular maximization under routing cost · Beijing air-quality mobile robotic sensingentropyHelped
use GCB instead of iterative submodular knapsack (ISK)submodular maximization under routing cost · Beijing mobile robotic sensingrunning timeHelped
use GCB instead of GR and ISKinfluence maximization with routing · San Diego door-to-door solar marketingexpected influenceHelped
use GCB instead of GR and ISKinfluence maximization with routing · BA social + ER routing random graphsexpected influenceMixed
increase routing network density pISK · random graphsrunning timeHurt

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy