arXiv subjects
Publications and source records attributed to Nils Lommen.
KoAT is a tool to automatically infer complexity bounds and prove termination of (possibly recursive) integer programs. To this end, KoAT implements an alternating modular inference of upper runtime and size bounds for program parts. In particular, KoAT uses a portfolio of different techniques to analyze subprograms. The power of our approach is demonstrated by an extensive experimental evaluation.
We show that existing tools for termination analysis are extremely well suited for LTL model checking of infinite state systems. To this end, we present a framework MoAT which uses the well-known automata-based approach and reduces the LTL model checking problem to fair termination. To prove or disprove fair termination, it then calls the termination tools KoAT and LoAT in the backend. Our experiments show that in this way, MoAT is on par with existing state-of-the-art tools for LTL model checking of infinite state systems.
Solving quantifier-free non-linear real arithmetic (NRA) problems is a computationally hard task. To tackle this problem, prior work proposed a promising approach based on gradient descent. In this work, we extend their ideas and combine LLMs and GPU acceleration to obtain an efficient technique. We have implemented our findings in the novel SMT solver GANRA (GPU Accelerated solving of Nonlinear Real Arithmetic problems). We evaluate GANRA on two different NRA benchmarks and demonstrate significant improvements over the previous state of the art. In particular, on the Sturm-MBO benchmark, we can prove satisfiability for more than five times as many instances in less than 1/20th of the previous state-of-the-art runtime.
We consider linear single-path loops of the form \[ \textbf{while} \quad \varphi \quad \textbf{do} \quad \vec{x} \gets A \vec{x} + \vec{b} \quad \textbf{end} \] where $\vec{x}$ is a vector of variables, the loop guard $\varphi$ is a conjunction of linear inequations over the variables $\vec{x}$, and the update of the loop is represented by the matrix $A$ and the vector $\vec{b}$. It is already known that termination of such loops is decidable. In this work, we consider loops where $A$ has real eigenvalues, and prove that it is decidable whether the loop's runtime (for all inputs) is bounded by a constant if the variables range over $\mathbb R$ or $\mathbb Q$. This is an important problem in automatic program verification, since safety of linear while-programs is decidable if all loops have constant runtime, and it is closely connected to the existence of multiphase-linear ranking functions, which are often used for termination and complexity analysis. To evaluate its practical applicability, we also present an implementation of our decision procedure.
In earlier work, we developed a modular approach for automatic complexity analysis of integer programs. However, these integer programs do not allow non-tail recursive calls or subprocedures. In this paper, we consider integer programs with function calls and present a natural extension of our modular complexity analysis approach to the recursive setting based on a new form of ranking functions. Hence, our approach combines already existing powerful techniques on the "imperative" parts of the program and our novel ranking functions on the recursive parts. The strength of this combination is demonstrated by our implementation in the complexity analysis tool KoAT.
There exist several approaches to infer runtime or resource bounds for integer programs automatically. In this paper, we study the subclass of periodic rational solvable loops (prs-loops), where questions regarding the runtime and the size of variable values are decidable and where we can therefore obtain techniques that are complete for such subclasses. We show how to use these results for the complexity analysis of arbitrary general integer programs. To this end, we present a modular approach which computes local runtime and size bounds for subprograms which correspond to prs-loops. These local bounds are then lifted to global runtime and size bounds for the whole integer program. Furthermore, we introduce several techniques to transform larger programs into prs-loops to increase the scope of the approach. The power of the procedure is shown by our implementation in the complexity analysis tool KoAT.
There exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach which computes local runtime bounds for subprograms which can be transformed into twn-loops. These local runtime bounds are then lifted to global runtime bounds for the whole program. The power of our approach is shown by our implementation in the tool KoAT which analyzes complexity of programs where all other state-of-the-art tools fail.
We present a new procedure to infer size bounds for integer programs automatically. Size bounds are important for the deduction of bounds on the runtime complexity or in general, for the resource analysis of programs. We show that our technique is complete (i.e., it always computes finite size bounds) for a subclass of loops, possibly with non-linear arithmetic. Moreover, we present a novel approach to combine and integrate this complete technique into an incomplete approach to infer size and runtime bounds of general integer programs. We prove completeness of our integration for an important subclass of integer programs. We implemented our new algorithm in the automated complexity analysis tool KoAT to evaluate its power, in particular on programs with non-linear arithmetic.
Recently, we showed how to use control-flow refinement (CFR) to improve automatic complexity analysis of integer programs. While up to now CFR was limited to classical programs, in this paper we extend CFR to probabilistic programs and show its soundness for complexity analysis. To demonstrate its benefits, we implemented our new CFR technique in our complexity analysis tool KoAT.
There exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach which computes local runtime bounds for subprograms which can be transformed into twn-loops. These local runtime bounds are then lifted to global runtime bounds for the whole program. The power of our approach is shown by our implementation in the tool KoAT which analyzes complexity of programs where all other state-of-the-art tools fail.
In earlier work, we developed an approach for automatic complexity analysis of integer programs, based on an alternating modular inference of upper runtime and size bounds for program parts. In this paper, we show how recent techniques to improve automated termination analysis of integer programs (like the generation of multiphase-linear ranking functions and control-flow refinement) can be integrated into our approach for the inference of runtime bounds. The power of the resulting approach is demonstrated by an extensive experimental evaluation with our new re-implementation of the corresponding tool KoAT.