Towards complete methods for automatic complexity and termination analysis of (probabilistic) programs
- Alternative title
-
Methoden für die automatische Komplexitäts- und Terminierungsanalyse von (probabilistischen) Programmen und ihre Vollständigkeit
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
-
Englisch
- Notes
-
RWTH Aachen University, Dissertation, 2021
- Keyword
-
Programs
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
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Universitätsbibliothek der RWTH Aachen
- (when)
-
2021
- Creator
- Contributor
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Giesl, Jürgen
Kovács, Laura
- DOI
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10.18154/RWTH-2021-06073
- URN
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urn:nbn:de:101:1-2021081806153418964312
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:41 PM CET
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hark, Marcel
- Giesl, Jürgen
- Kovács, Laura
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2021
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Termination proofs for logic programs
The Termination and Complexity Competition
Automated termination analysis of C programs
Automatic Complexity Analysis of Integer Programs via Triangular Weakly Non-Linear Loops
Proving Termination of C Programs with Lists
Control-Flow Refinement for Complexity Analysis of Probabilistic Programs in KoAT (Short Paper)
Verification of Reachability Properties and Termination for Probabilistic Systems
Automated Termination Analysis of Polynomial Probabilistic Programs
Automatic termination analysis for functional and imperative programs
Proving termination of probabilistic recursive programs via SMT-solving
The probabilistic termination tool amber
Termination proofs for logic programs
Termination proofs for logic programs
The Termination and Complexity Competition
Automated termination analysis of C programs
Automatic Complexity Analysis of Integer Programs via Triangular Weakly Non-Linear Loops
Proving Termination of C Programs with Lists
Control-Flow Refinement for Complexity Analysis of Probabilistic Programs in KoAT (Short Paper)