Numerical Verification of Affine Systems with up to a Billion Dimensions
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Affine systems reachability is the basis of many verification methods. With further computation, methods exist to reason about richer models that have inputs, nonlinear differential equations, and hybrid dynamics. As such, the scalability of affine systems verification is a prerequisite to the scalability of analysis methods for more complex systems. In this paper, we investigate these scalability limits, improving by several orders of magnitude the size of systems that can be analyzed. One benefit of affine systems is that their reachable states can be written in terms of the matrix exponential, and safety checking can be performed at specific time steps with linear programming. Unfortunately, for large systems with many state variables, this direct approach requires an intractable amount of memory while using an intractable amount of computation time. We overcome these two problems by combining several methods that leverage common problem structure. Memory demands can be reduced by taking advantage of both initial states that are not full-dimensional, and safety properties (outputs) that only need a few linear projections of the state variables. Computation time is saved by using numerical simulations to compute only projections of the matrix exponential relevant for the verification problem. Since large systems often have sparse dynamics, we use fast Krylov-subspace simulation methods based on the Arnoldi or Lanczos iterations. Our implementation produces accurate counter-examples when properties are violated and, with sufficient problem structure, can scale to analyze systems with up to a billion real-valued state variables.
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