11,718 research outputs found
Inferring Lower Runtime Bounds for Integer Programs
We present a technique to infer lower bounds on the worst-case runtime complexity of integer programs, where in contrast to earlier work, our approach is not restricted to tail-recursion. Our technique constructs symbolic representations of program executions using a framework for iterative, under-approximating program simplification. The core of this simplification is a method for (under-approximating) program acceleration based on recurrence solving and a variation of ranking functions. Afterwards, we deduce asymptotic lower bounds from the resulting simplified programs using a special-purpose calculus and an SMT encoding. We implemented our technique in our tool LoAT and show that it infers non-trivial lower bounds for a large class of examples
U-model based adaptive internal model control for tracking of nonlinear dynamic plants
We present a technique to infer lower bounds on the worst-case runtime
complexity of integer programs, where in contrast to earlier work, our approach
is not restricted to tail-recursion. Our technique constructs symbolic
representations of program executions using a framework for iterative,
under-approximating program simplification. The core of this simplification is
a method for (under-approximating) program acceleration based on recurrence
solving and a variation of ranking functions. Afterwards, we deduce asymptotic
lower bounds from the resulting simplified programs using a special-purpose
calculus and an SMT encoding. We implemented our technique in our tool LoAT and
show that it infers non-trivial lower bounds for a large class of examples
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Specialising finite domain programs with polyhedra
A procedure is described for tightening domain constraints of finite domain logic programs by applying a static analysis based on convex polyhedra. Individual finite domain constraints are over-approximated by polyhedra to describe the solution space over ninteger variables as an n dimensional polyhedron. This polyhedron is then approximated, using projection, as an n dimensional bounding box that can be used to specialise and improve the domain constraints. The analysis can be implemented straightforwardly and an empirical evaluation of the specialisation technique is given
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF
inference problems. The core of our method is a very efficient bounding
procedure, which combines scalable semidefinite programming (SDP) and a
cutting-plane method for seeking violated constraints. In order to further
speed up the computation, several strategies have been exploited, including
model reduction, warm start and removal of inactive constraints.
We analyze the performance of the proposed method under different settings,
and demonstrate that our method either outperforms or performs on par with
state-of-the-art approaches. Especially when the connectivities are dense or
when the relative magnitudes of the unary costs are low, we achieve the best
reported results. Experiments show that the proposed algorithm achieves better
approximation than the state-of-the-art methods within a variety of time
budgets on challenging non-submodular MAP-MRF inference problems.Comment: 21 page
A Formal, Resource Consumption-Preserving Translation of Actors to Haskell
We present a formal translation of an actor-based language with cooperative
scheduling to the functional language Haskell. The translation is proven
correct with respect to a formal semantics of the source language and a
high-level operational semantics of the target, i.e. a subset of Haskell. The
main correctness theorem is expressed in terms of a simulation relation between
the operational semantics of actor programs and their translation. This allows
us to then prove that the resource consumption is preserved over this
translation, as we establish an equivalence of the cost of the original and
Haskell-translated execution traces.Comment: Pre-proceedings paper presented at the 26th International Symposium
on Logic-Based Program Synthesis and Transformation (LOPSTR 2016), Edinburgh,
Scotland UK, 6-8 September 2016 (arXiv:1608.02534
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