31 research outputs found
Polynomial tuning of multiparametric combinatorial samplers
Boltzmann samplers and the recursive method are prominent algorithmic
frameworks for the approximate-size and exact-size random generation of large
combinatorial structures, such as maps, tilings, RNA sequences or various
tree-like structures. In their multiparametric variants, these samplers allow
to control the profile of expected values corresponding to multiple
combinatorial parameters. One can control, for instance, the number of leaves,
profile of node degrees in trees or the number of certain subpatterns in
strings. However, such a flexible control requires an additional non-trivial
tuning procedure. In this paper, we propose an efficient polynomial-time, with
respect to the number of tuned parameters, tuning algorithm based on convex
optimisation techniques. Finally, we illustrate the efficiency of our approach
using several applications of rational, algebraic and P\'olya structures
including polyomino tilings with prescribed tile frequencies, planar trees with
a given specific node degree distribution, and weighted partitions.Comment: Extended abstract, accepted to ANALCO2018. 20 pages, 6 figures,
colours. Implementation and examples are available at [1]
https://github.com/maciej-bendkowski/boltzmann-brain [2]
https://github.com/maciej-bendkowski/multiparametric-combinatorial-sampler
On the enumeration of closures and environments with an application to random generation
Environments and closures are two of the main ingredients of evaluation in
lambda-calculus. A closure is a pair consisting of a lambda-term and an
environment, whereas an environment is a list of lambda-terms assigned to free
variables. In this paper we investigate some dynamic aspects of evaluation in
lambda-calculus considering the quantitative, combinatorial properties of
environments and closures. Focusing on two classes of environments and
closures, namely the so-called plain and closed ones, we consider the problem
of their asymptotic counting and effective random generation. We provide an
asymptotic approximation of the number of both plain environments and closures
of size . Using the associated generating functions, we construct effective
samplers for both classes of combinatorial structures. Finally, we discuss the
related problem of asymptotic counting and random generation of closed
environemnts and closures
Combinatorics of explicit substitutions
is an extension of the -calculus which
internalises the calculus of substitutions. In the current paper, we
investigate the combinatorial properties of focusing on the
quantitative aspects of substitution resolution. We exhibit an unexpected
correspondence between the counting sequence for -terms and
famous Catalan numbers. As a by-product, we establish effective sampling
schemes for random -terms. We show that typical
-terms represent, in a strong sense, non-strict computations
in the classic -calculus. Moreover, typically almost all substitutions
are in fact suspended, i.e. unevaluated, under closures. Consequently, we argue
that is an intrinsically non-strict calculus of explicit
substitutions. Finally, we investigate the distribution of various redexes
governing the substitution resolution in and investigate the
quantitative contribution of various substitution primitives
Statistical properties of lambda terms
We present a quantitative, statistical analysis of random lambda terms in the
de Bruijn notation. Following an analytic approach using multivariate
generating functions, we investigate the distribution of various combinatorial
parameters of random open and closed lambda terms, including the number of
redexes, head abstractions, free variables or the de Bruijn index value
profile. Moreover, we conduct an average-case complexity analysis of finding
the leftmost-outermost redex in random lambda terms showing that it is on
average constant. The main technical ingredient of our analysis is a novel
method of dealing with combinatorial parameters inside certain infinite,
algebraic systems of multivariate generating functions. Finally, we briefly
discuss the random generation of lambda terms following a given skewed
parameter distribution and provide empirical results regarding a series of more
involved combinatorial parameters such as the number of open subterms and
binding abstractions in closed lambda terms.Comment: Major revision of section 5. In particular, proofs of Lemma 5.7 and
Theorem 5.
On the enumeration of closures and environments with an application to random generation
Environments and closures are two of the main ingredients of evaluation in
lambda-calculus. A closure is a pair consisting of a lambda-term and an
environment, whereas an environment is a list of lambda-terms assigned to free
variables. In this paper we investigate some dynamic aspects of evaluation in
lambda-calculus considering the quantitative, combinatorial properties of
environments and closures. Focusing on two classes of environments and
closures, namely the so-called plain and closed ones, we consider the problem
of their asymptotic counting and effective random generation. We provide an
asymptotic approximation of the number of both plain environments and closures
of size . Using the associated generating functions, we construct effective
samplers for both classes of combinatorial structures. Finally, we discuss the
related problem of asymptotic counting and random generation of closed
environemnts and closures
Statistical properties of lambda terms
We present a quantitative, statistical analysis of random lambda terms in the De Bruijn notation. Following an analytic approach using multivariate generat-ing functions, we investigate the distribution of various combinatorial parameters of random open and closed lambda terms, including the number of redexes, head abstractions, free variables or the De Bruijn index value profile. Moreover, we con-duct an average-case complexity analysis of finding the leftmost-outermost redex in random lambda terms showing that it is on average constant. The main technical
ingredient of our analysis is a novel method of dealing with combinatorial paramet-ers inside certain infinite, algebraic systems of multivariate generating functions. Finally, we briefly discuss the random generation of lambda terms following a given skewed parameter distribution and provide empirical results regarding a series of more involved combinatorial parameters such as the number of open subterms and binding abstractions in closed lambda terms