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The Structure of Differential Invariants and Differential Cut Elimination
, 2011
"... not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution or government. Keywords: Proof theory, differential equations, differential cut elimination, logics of programs, The biggest challenge in hybrid systems verification is the handling o ..."
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not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution or government. Keywords: Proof theory, differential equations, differential cut elimination, logics of programs, The biggest challenge in hybrid systems verification is the handling of differential equations. Because computable closedform solutions only exist for very simple differential equations, proof certificates have been proposed for more scalable verification. Search procedures for these proof certificates are still rather adhoc, though, because the problem structure is only understood poorly. We investigate differential invariants, which can be checked for invariance along a differential equation just by using their differential structure and without having to solve the differential equation. We study the structural properties of differential invariants. To analyze tradeoffs for proof search complexity, we identify more than a dozen relations between several classes of differential invariants and compare their deductive power. As our main results, we analyze the deductive power of differential cuts and the deductive power of differential invariants with auxiliary differential variables. We refute the differential cut elimination hypothesis and show that differential cuts are fundamental proof principles that strictly increase the deductive power. We also prove that
A survey on continuous time computations
 New Computational Paradigms
"... Abstract. We provide an overview of theories of continuous time computation. These theories allow us to understand both the hardness of questions related to continuous time dynamical systems and the computational power of continuous time analog models. We survey the existing models, summarizing resu ..."
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Abstract. We provide an overview of theories of continuous time computation. These theories allow us to understand both the hardness of questions related to continuous time dynamical systems and the computational power of continuous time analog models. We survey the existing models, summarizing results, and point to relevant references in the literature. 1
The general purpose analog computer and computable analysis are two equivalent paradigms of analog computation
 Theory and Applications of Models of Computation, Third International Conference, TAMC 2006
, 2006
"... Abstract. In this paper we revisit one of the first models of analog computation, Shannon’s General Purpose Analog Computer (GPAC). The GPAC has often been argued to be weaker than computable analysis. As main contribution, we show that if we change the notion of GPACcomputability in a natural way, ..."
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Cited by 10 (2 self)
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Abstract. In this paper we revisit one of the first models of analog computation, Shannon’s General Purpose Analog Computer (GPAC). The GPAC has often been argued to be weaker than computable analysis. As main contribution, we show that if we change the notion of GPACcomputability in a natural way, we compute exactly all real computable functions (in the sense of computable analysis). Moreover, since GPACs are equivalent to systems of polynomial differential equations then we show that all real computable functions can be defined by such models. 1
The Elementary Computable Functions over the Real Numbers: Applying Two New Techniques. Archives for Mathematical Logic
, 2008
"... The basic motivation behind this work is to tie together various computational complexity classes, whether over different domains such as the naturals or the reals, or whether defined in different manners, via function algebras (Real Recursive Functions) or via Turing Machines (Computable Analysis). ..."
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Cited by 9 (4 self)
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The basic motivation behind this work is to tie together various computational complexity classes, whether over different domains such as the naturals or the reals, or whether defined in different manners, via function algebras (Real Recursive Functions) or via Turing Machines (Computable Analysis). We provide general tools for investigating these issues, using two techniques we call approximation and lifting. We use these methods to obtain two main theorems. First we provide an alternative proof of the result from Campagnolo, Moore and Costa [3], which precisely relates the Kalmar elementary computable functions to a function algebra over the reals. Secondly, we build on that result to extend a result of Bournez and Hainry [1], which provided a function algebra for the C2 real elementary computable functions; our result does not require the restriction to C2 functions. In addition to the extension, we provide an alternative approach to the proof. Their proof involves simulating the operation of a Turing Machine using a function algebra. We avoid this simulation, using a technique we call lifting, which allows us to lift the classic result regarding the elementary computable functions to a result on the reals. The two new techniques bring a different perspective to these problems, and furthermore appear more easily applicable to other problems of this sort. 1
Abstract geometrical computation and computable analysis
 Int. Conf. on Unconventional Computation 2009 (UC ’09), number 5715 in LNCS
"... Abstract. Extended Signal machines are proven able to compute any computable function in the understanding of recursive/computable analysis (CA), here type2 Turing machines (T2TM) with signed binary encoding. This relies on an intermediate representation of any real number as an integer (in signed ..."
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Cited by 6 (0 self)
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Abstract. Extended Signal machines are proven able to compute any computable function in the understanding of recursive/computable analysis (CA), here type2 Turing machines (T2TM) with signed binary encoding. This relies on an intermediate representation of any real number as an integer (in signed binary) plus an exact value in (−1,1) which allows to have only finitely many signals present outside of the computation. Extracting a (signed) bit, improving the precision by one bit and iterating the T2TM only involve standard signal machines. For exact CAcomputations, T2TM have to deal with an infinite entry and to run through infinitely many iterations to produce an infinite output. This infinite duration can be provided by constructions emulating the black hole model of computation on an extended signal machine. Extracting/encoding an infinite sequence of bits is achieved as the limit of the approximation process with a careful handling of accumulations and singularities. Keywords. Analog computation; Abstract geometrical computation; Computable analysis; Signal machine; Type2 Turing machine. 1
Using approximation to relate computational classes over the reals
 MCU 2007, Lecture Notes in Computer Science 4664 (2007
"... Abstract. We use our method of approximation to relate various classes of computable functions over the reals. In particular, we compare Computable Analysis to the two analog models, the General Purpose Analog Computer and Real Recursive Functions. There are a number of existing results in the lite ..."
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Abstract. We use our method of approximation to relate various classes of computable functions over the reals. In particular, we compare Computable Analysis to the two analog models, the General Purpose Analog Computer and Real Recursive Functions. There are a number of existing results in the literature showing that the different models correspond exactly. We show how these exact correspondences can be broken down into a two step process of approximation and completion. We show that the method of approximation has further application in relating classes of functions, exploiting the transitive nature of the approximation relation. This work builds on our earlier work with our method of approximation, giving more evidence of the breadth of its applicability. 1
Stability of polynomial differential equations: Complexity and converse lyapunov questions
 CoRR
"... Stability analysis of polynomial differential equations is a central topic in nonlinear dynamics and control which in recent years has undergone major algorithmic developments due to advances in optimization theory. Notably, the last decade has seen a widespread interest in the use of sum of squares ..."
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Stability analysis of polynomial differential equations is a central topic in nonlinear dynamics and control which in recent years has undergone major algorithmic developments due to advances in optimization theory. Notably, the last decade has seen a widespread interest in the use of sum of squares (sos) based semidefinite programs that can automatically find polynomial Lyapunov functions and produce explicit certificates of stability. However, despite their popularity, the converse question of whether such algebraic, efficiently constructable certificates of stability always exist has remained elusive. Naturally, an algorithmic question of this nature is closely intertwined with the fundamental computational complexity of proving stability. In this paper, we make a number of contributions to the questions of (i) complexity of deciding stability, (ii) existence of polynomial Lyapunov functions, and (iii) existence of sos Lyapunov functions. (i) We show that deciding local or global asymptotic stability of cubic vector fields is strongly NPhard. Simple variations of our proof are shown to imply strong NPhardness of several other decision problems: testing local attractivity of an equilibrium point, stability of an equilibrium point in the sense of Lyapunov, invariance of the unit ball, boundedness of trajectories, conver
Characterizing Computable Analysis with Differential Equations
, 2008
"... The functions of Computable Analysis are defined by enhancing the capacities of normal Turing Machines to deal with real number inputs. We consider characterizations of these functions using function algebras, known as Real Recursive Functions. Bournez and Hainry 2006 [5] used a function algebra to ..."
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The functions of Computable Analysis are defined by enhancing the capacities of normal Turing Machines to deal with real number inputs. We consider characterizations of these functions using function algebras, known as Real Recursive Functions. Bournez and Hainry 2006 [5] used a function algebra to characterize the twice continuously differentiable functions of Computable Analysis, restricted to certain compact domains. In a similar model, Shannon’s General Purpose Analog Computer, Bournez et. al. 2007 [3] also characterize the functions of Computable Analysis. We combine the results of [5] and Graça et. al. [13], to show that a different function algebra also yields Computable Analysis. We believe that our function algebra is an improvement due to its simple definition and because the operations in our algebra are less obviously designed to mimic the operations in the usual definition of the recursive functions using the primitive recursion and minimization operators. 1
Implicit complexity in recursive analysis
 TENTH INTERNATIONAL WORKSHOP ON LOGIC AND COMPUTATIONAL COMPLEXITY LCC'09
, 2009
"... Recursive analysis is a model of analog computation which is based on type 2 Turing machines. Various classes of functions computable in recursive analysis have recently been characterized in a machine independent and algebraical context. In particular nice connections between the class of computabl ..."
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Recursive analysis is a model of analog computation which is based on type 2 Turing machines. Various classes of functions computable in recursive analysis have recently been characterized in a machine independent and algebraical context. In particular nice connections between the class of computable functions (and some of its sub and supclasses) over the reals and algebraically defined (sub and sup) classes of Rrecursive functions à la Moore have been obtained. We provide in this paper a framework that allows to dive into complexity for functions over the reals. It indeed relates classical computability and complexity classes with the corresponding classes in recursive analysis. This framework opens the field of implicit complexity of functions over the reals. While our setting provides a new reading of some of the existing characterizations, it also provides new results: inspired by Bellantoni and Cook’s characterization of polynomial time computable functions, we provide the first algebraic characterization of polynomial time computable functions over the reals.