DynamiTe: dynamic termination and non-termination proofs

Abstract

There is growing interest in termination reasoning for nonlinear programs and, meanwhile, recent dynamic strategies have shown they are able to infer invariants for such challenging programs. These advances led us to hypothesize that perhaps such dynamic strategies for nonlinear invariants could be adapted to learn recurrent sets (for non-termination) and/or ranking functions (for termination).

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 13, 2020
Source ID
10.1145/3428257

Entities

People

  • Eric Koskinen
  • Parisa Fathololumi
  • ThanhVu Nguyen
  • Timos Antonopoulos
  • Ton Chanh Le

Organizations

  • Army Research Office
  • National Science Foundation
  • Office of Naval Research
  • Stevens Institute of Technology
  • University of Nebraska–Lincoln
  • Yale University

Tags

Readers

  • Materials Science and Engineering.
  • Neural Network Machine Learning.
  • Rocket Propulsion.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms