320 research outputs found
Leaderless deterministic chemical reaction networks
This paper answers an open question of Chen, Doty, and Soloveichik [1], who
showed that a function f:N^k --> N^l is deterministically computable by a
stochastic chemical reaction network (CRN) if and only if the graph of f is a
semilinear subset of N^{k+l}. That construction crucially used "leaders": the
ability to start in an initial configuration with constant but non-zero counts
of species other than the k species X_1,...,X_k representing the input to the
function f. The authors asked whether deterministic CRNs without a leader
retain the same power.
We answer this question affirmatively, showing that every semilinear function
is deterministically computable by a CRN whose initial configuration contains
only the input species X_1,...,X_k, and zero counts of every other species. We
show that this CRN completes in expected time O(n), where n is the total number
of input molecules. This time bound is slower than the O(log^5 n) achieved in
[1], but faster than the O(n log n) achieved by the direct construction of [1]
(Theorem 4.1 in the latest online version of [1]), since the fast construction
of that paper (Theorem 4.4) relied heavily on the use of a fast, error-prone
CRN that computes arbitrary computable functions, and which crucially uses a
leader.Comment: arXiv admin note: substantial text overlap with arXiv:1204.417
Efficient size estimation and impossibility of termination in uniform dense population protocols
We study uniform population protocols: networks of anonymous agents whose
pairwise interactions are chosen at random, where each agent uses an identical
transition algorithm that does not depend on the population size . Many
existing polylog time protocols for leader election and majority
computation are nonuniform: to operate correctly, they require all agents to be
initialized with an approximate estimate of (specifically, the exact value
). Our first main result is a uniform protocol for
calculating with high probability in time and
states ( bits of memory). The protocol is
converging but not terminating: it does not signal when the estimate is close
to the true value of . If it could be made terminating, this would
allow composition with protocols, such as those for leader election or
majority, that require a size estimate initially, to make them uniform (though
with a small probability of failure). We do show how our main protocol can be
indirectly composed with others in a simple and elegant way, based on the
leaderless phase clock, demonstrating that those protocols can in fact be made
uniform. However, our second main result implies that the protocol cannot be
made terminating, a consequence of a much stronger result: a uniform protocol
for any task requiring more than constant time cannot be terminating even with
probability bounded above 0, if infinitely many initial configurations are
dense: any state present initially occupies agents. (In particular,
no leader is allowed.) Crucially, the result holds no matter the memory or time
permitted. Finally, we show that with an initial leader, our size-estimation
protocol can be made terminating with high probability, with the same
asymptotic time and space bounds.Comment: Using leaderless phase cloc
Composable computation in discrete chemical reaction networks
We study the composability of discrete chemical reaction networks (CRNs) that
stably compute (i.e., with probability 0 of error) integer-valued functions
. We consider output-oblivious CRNs in which the
output species is never a reactant (input) to any reaction. The class of
output-oblivious CRNs is fundamental, appearing in earlier studies of CRN
computation, because it is precisely the class of CRNs that can be composed by
simply renaming the output of the upstream CRN to match the input of the
downstream CRN.
Our main theorem precisely characterizes the functions stably computable
by output-oblivious CRNs with an initial leader. The key necessary condition is
that for sufficiently large inputs, is the minimum of a finite number of
nondecreasing quilt-affine functions. (An affine function is linear with a
constant offset; a quilt-affine function is linear with a periodic offset)
Composable Computation in Leaderless, Discrete Chemical Reaction Networks
We classify the functions f:?^d ? ? that are stably computable by leaderless, output-oblivious discrete (stochastic) Chemical Reaction Networks (CRNs). CRNs that compute such functions are systems of reactions over species that include d designated input species, whose initial counts represent an input x ? ?^d, and one output species whose eventual count represents f(x). Chen et al. showed that the class of functions computable by CRNs is precisely the semilinear functions. In output-oblivious CRNs, the output species is never a reactant. Output-oblivious CRNs are easily composable since a downstream CRN can consume the output of an upstream CRN without affecting its correctness. Severson et al. showed that output-oblivious CRNs compute exactly the subclass of semilinear functions that are eventually the minimum of quilt-affine functions, i.e., affine functions with different intercepts in each of finitely many congruence classes. They call such functions the output-oblivious functions. A leaderless CRN can compute only superadditive functions, and so a leaderless output-oblivious CRN can compute only superadditive, output-oblivious functions. In this work we show that a function f:?^d ? ? is stably computable by a leaderless, output-oblivious CRN if and only if it is superadditive and output-oblivious
Hardness of Computing and Approximating Predicates and Functions with Leaderless Population Protocols
Population protocols are a distributed computing model appropriate for describing massive numbers of agents with very limited computational power (finite automata in this paper), such as sensor networks or programmable chemical reaction networks in synthetic biology. A population protocol is said to require a leader if every valid initial configuration contains a single agent in a special "leader" state that helps to coordinate the computation. Although the class of predicates and functions computable with probability 1 (stable computation) is the same whether a leader is required or not (semilinear functions and predicates), it is not known whether a leader is necessary for fast computation. Due to the large number of agents n (synthetic molecular systems routinely have trillions of molecules), efficient population protocols are generally defined as those computing in polylogarithmic in n (parallel) time. We consider population protocols that start in leaderless initial configurations, and the computation is regarded finished when the population protocol reaches a configuration from which a different output is no longer reachable.
In this setting we show that a wide class of functions and predicates computable by population protocols are not efficiently computable (they require at least linear time), nor are some linear functions even efficiently approximable. It requires at least linear time for a population protocol even to approximate division by a constant or subtraction (or any linear function with a coefficient outside of N), in the sense that for sufficiently small gamma > 0, the output of a sublinear time protocol can stabilize outside the interval f(m) (1 +/- gamma) on infinitely many inputs m. In a complementary positive result, we show that with a sufficiently large value of gamma, a population protocol can approximate any linear f with nonnegative rational coefficients, within approximation factor gamma, in O(log n) time. We also show that it requires linear time to exactly compute a wide range of semilinear functions (e.g., f(m)=m if m is even and 2m if m is odd) and predicates (e.g., parity, equality)
Message Complexity of Population Protocols
The standard population protocol model assumes that when two agents interact,
each observes the entire state of the other agent. We initiate the study of
for population protocols, where the state of an
agent is divided into an externally-visible and an internal
component, where only the message can be observed by the other agent in an
interaction.
We consider the case of message complexity. When time is unrestricted,
we obtain an exact characterization of the stably computable predicates based
on the number of internal states : If then the protocol
computes semilinear predicates (unlike the original model, which can compute
non-semilinear predicates with ), and otherwise it computes a
predicate decidable by a nondeterministic -space-bounded Turing
machine. We then introduce novel expected time
protocols for junta/leader election and general purpose broadcast correct with
high probability, and approximate and exact population size counting correct
with probability 1. Finally, we show that the main constraint on the power of
bounded-message-size protocols is the size of the internal states: with
unbounded internal states, any computable function can be computed with
probability 1 in the limit by a protocol that uses only
messages
On the Runtime of Chemical Reaction Networks Beyond Idealized Conditions
This paper studies the (discrete) chemical reaction network (CRN) computational model that emerged in the last two decades as an abstraction for molecular programming. The correctness of CRN protocols is typically established under one of two possible schedulers that determine how the execution advances: (1) a stochastic scheduler that obeys the (continuous time) Markov process dictated by the standard model of stochastic chemical kinetics; or (2) an adversarial scheduler whose only commitment is to maintain a certain fairness condition. The latter scheduler is justified by the fact that the former one crucially assumes "idealized conditions" that more often than not, do not hold in real wet-lab experiments. However, when it comes to analyzing the runtime of CRN protocols, the existing literature focuses strictly on the stochastic scheduler, thus raising the research question that drives this work: Is there a meaningful way to quantify the runtime of CRNs without the idealized conditions assumption?
The main conceptual contribution of the current paper is to answer this question in the affirmative, formulating a new runtime measure for CRN protocols that does not rely on idealized conditions. This runtime measure is based on an adapted (weaker) fairness condition as well as a novel scheme that enables partitioning the execution into short rounds and charging the runtime for each round individually (inspired by definitions for the runtime of asynchronous distributed algorithms). Following that, we turn to investigate various fundamental computational tasks and establish (often tight) bounds on the runtime of the corresponding CRN protocols operating under the adversarial scheduler. This includes an almost complete chart of the runtime complexity landscape of predicate decidability tasks
Optimal Information Encoding in Chemical Reaction Networks
Discrete chemical reaction networks formalize the interactions of molecular species in a well-mixed solution as stochastic events. Given their basic mathematical and physical role, the computational power of chemical reaction networks has been widely studied in the molecular programming and distributed computing communities. While for Turing-universal systems there is a universal measure of optimal information encoding based on Kolmogorov complexity, chemical reaction networks are not Turing universal unless error and unbounded molecular counts are permitted. Nonetheless, here we show that the optimal number of reactions to generate a specific count x ? ? with probability 1 is asymptotically equal to a "space-aware" version of the Kolmogorov complexity of x, defined as K?s(x) = min_p {|p|/log|p| + log(space(?(p))) : ?(p) = x}, where p is a program for universal Turing machine ?. This version of Kolmogorov complexity incorporates not just the length of the shortest program for generating x, but also the space usage of that program. Probability 1 computation is captured by the standard notion of stable computation from distributed computing, but we limit our consideration to chemical reaction networks obeying a stronger constraint: they "know when they are done" in the sense that they produce a special species to indicate completion. As part of our results, we develop a module for encoding and unpacking any b bits of information via O(b/log{b}) reactions, which is information-theoretically optimal for incompressible information. Our work provides one answer to the question of how succinctly chemical self-organization can be encoded - in the sense of generating precise molecular counts of species as the desired state
Space-Optimal Majority in Population Protocols
Population protocols are a model of distributed computing, in which
agents with limited local state interact randomly, and cooperate to
collectively compute global predicates. An extensive series of papers, across
different communities, has examined the computability and complexity
characteristics of this model. Majority, or consensus, is a central task, in
which agents need to collectively reach a decision as to which one of two
states or had a higher initial count. Two complexity metrics are
important: the time that a protocol requires to stabilize to an output
decision, and the state space size that each agent requires.
It is known that majority requires states per agent to
allow for poly-logarithmic time stabilization, and that states
are sufficient. Thus, there is an exponential gap between the upper and lower
bounds.
We address this question. We provide a new lower bound of
states for any protocol which stabilizes in time, for any constant. This result is conditional on basic monotonicity and output
assumptions, satisfied by all known protocols. Technically, it represents a
significant departure from previous lower bounds. Instead of relying on dense
configurations, we introduce a new surgery technique to construct executions
which contradict the correctness of algorithms that stabilize too fast.
Subsequently, our lower bound applies to general initial configurations.
We give an algorithm for majority which uses states, and
stabilizes in time. Central to the algorithm is a new leaderless
phase clock, which allows nodes to synchronize in phases of
consecutive interactions using states per node. We also employ our
phase clock to build a leader election algorithm with states,
which stabilizes in time
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