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Register a new userPseudodeterministic Algorithms and the Structure of Probabilistic Time
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Added by
Zhenjian Lu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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We connect the study of pseudodeterministic algorithms to two major open problems about the structural complexity of $mathsf{BPTIME}$: proving hierarchy theorems and showing the existence of complete problems. Our main contributions can be summarised as follows. 1. We build on techniques developed to prove hierarchy theorems for probabilistic time with advice (Fortnow and Santhanam, FOCS 2004) to construct the first unconditional pseudorandom generator of polynomial stretch computable in pseudodeterministic polynomial time (with one bit of advice) that is secure infinitely often against polynomial-time computations. As an application of this construction, we obtain new results about the complexity of generating and representing prime numbers. 2. Oliveira and Santhanam (STOC 2017) established unconditionally that there is a pseudodeterministic algorithm for the Circuit Acceptance Probability Problem ($mathsf{CAPP}$) that runs in sub-exponential time and is correct with high probability over any samplable distribution on circuits on infinitely many input lengths. We show that improving this running time or obtaining a result that holds for every large input length would imply new time hierarchy theorems for probabilistic time. In addition, we prove that a worst-case polynomial-time pseudodeterministic algorithm for $mathsf{CAPP}$ would imply that $mathsf{BPP}$ has complete problems. 3. We establish an equivalence between pseudodeterministic construction of strings of large $mathsf{rKt}$ complexity (Oliveira, ICALP 2019) and the existence of strong hierarchy theorems for probabilistic time. More generally, these results suggest new approaches for designing pseudodeterministic algorithms for search problems and for unveiling the structure of probabilistic time.
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Boolean functions can be represented in many ways including logical forms, truth tables, and polynomials. Additionally, Boolean functions have different canonical representations such as minimal disjunctive normal forms. Other canonical representation is based on the polynomial representation of Boolean functions where they can be written as a nested product of canalizing layers and a polynomial that contains the noncanalizing variables. In this paper we study the problem of identifying the canalizing layers format of Boolean functions. First, we show that the problem of finding the canalizing layers is NP-hard. Second, we present several algorithms for finding the canalizing layers of a Boolean function, discuss their complexities, and compare their performances. Third, we show applications where the computation of canalizing layers can be used for finding a disjunctive normal form of a nested canalizing function. Another application deals with the reverse engineering of Boolean networks with a prescribed layering format. Finally, implementations of our algorithms in Python and in the computer algebra system Macaulay2 are available at https://github.com/ckadelka/BooleanCanalization.
We propose models for lobbying in a probabilistic environment, in which an actor (called The Lobby) seeks to influence voters preferences of voting for or against multiple issues when the voters preferences are represented in terms of probabilities. In particular, we provide two evaluation criteria and two bribery methods to formally describe these models, and we consider the resulting forms of lobbying with and without issue weighting. We provide a formal analysis for these problems of lobbying in a stochastic environment, and determine their classical and parameterized complexity depending on the given bribery/evaluation criteria and on various natural parameterizations. Specifically, we show that some of these problems can be solved in polynomial time, some are NP-complete but fixed-parameter tractable, and some are W[2]-complete. Finally, we provide approximability and inapproximability results for these problems and several variants.
Let $mathcal{C}$ and $mathcal{D}$ be hereditary graph classes. Consider the following problem: given a graph $Ginmathcal{D}$, find a largest, in terms of the number of vertices, induced subgraph of $G$ that belongs to $mathcal{C}$. We prove that it can be solved in $2^{o(n)}$ time, where $n$ is the number of vertices of $G$, if the following conditions are satisfied: * the graphs in $mathcal{C}$ are sparse, i.e., they have linearly many edges in terms of the number of vertices; * the graphs in $mathcal{D}$ admit balanced separators of size governed by their density, e.g., $mathcal{O}(Delta)$ or $mathcal{O}(sqrt{m})$, where $Delta$ and $m$ denote the maximum degree and the number of edges, respectively; and * the considered problem admits a single-exponential fixed-parameter algorithm when parameterized by the treewidth of the input graph. This leads, for example, to the following corollaries for specific classes $mathcal{C}$ and $mathcal{D}$: * a largest induced forest in a $P_t$-free graph can be found in $2^{tilde{mathcal{O}}(n^{2/3})}$ time, for every fixed $t$; and * a largest induced planar graph in a string graph can be found in $2^{tilde{mathcal{O}}(n^{3/4})}$ time.
In the design of probabilistic timed systems, bounded requirements concerning behaviour that occurs within a given time, energy, or more generally cost budget are of central importance. Traditionally, such requirements have been model-checked via a reduction to the unbounded case by unfolding the model according to the cost bound. This exacerbates the state space explosion problem and significantly increases runtime. In this paper, we present three new algorithms to model-check time- and cost-bounded properties for Markov decision processes and probabilistic timed automata that avoid unfolding. They are based on a modified value iteration process, on an enumeration of schedulers, and on state elimination techniques. We can now obtain results for any cost bound on a single state space no larger than for the corresponding unbounded or expected-value property. In particular, we can naturally compute the cumulative distribution function at no overhead. We evaluate the applicability and compare the performance of our new algorithms and their implementation on a number of case studies from the literature.
We study the probabilistic degree over reals of the OR function on $n$ variables. For an error parameter $epsilon$ in (0,1/3), the $epsilon$-error probabilistic degree of any Boolean function $f$ over reals is the smallest non-negative integer $d$ such that the following holds: there exists a distribution $D$ of polynomials entirely supported on polynomials of degree at most $d$ such that for all $z in {0,1}^n$, we have $Pr_{P sim D} [P(z) = f(z) ] geq 1- epsilon$. It is known from the works of Tarui ({Theoret. Comput. Sci.} 1993) and Beigel, Reingold, and Spielman ({ Proc. 6th CCC} 1991), that the $epsilon$-error probabilistic degree of the OR function is at most $O(log n.log 1/epsilon)$. Our first observation is that this can be improved to $O{log {{n}choose{leq log 1/epsilon}}}$, which is better for small values of $epsilon$. In all known constructions of probabilistic polynomials for the OR function (including the above improvement), the polynomials $P$ in the support of the distribution $D$ have the following special structure:$P = 1 - (1-L_1).(1-L_2)...(1-L_t)$, where each $L_i(x_1,..., x_n)$ is a linear form in the variables $x_1,...,x_n$, i.e., the polynomial $1-P(x_1,...,x_n)$ is a product of affine forms. We show that the $epsilon$-error probabilistic degree of OR when restricted to polynomials of the above form is $Omega ( log a/log^2 a )$ where $a = log {{n}choose{leq log 1/epsilon}}$. Thus matching the above upper bound (up to poly-logarithmic factors).
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