Tissue P systems with cell separation: attacking the partition problem

Tissue P systems are distributed parallel and non-deterministic computing models in the framework of membrane computing, which are inspired by intercellular communication and cooperation between neurons. Recently, cell separation is introduced into tissue P systems, which enables systems to generate an exponential workspace in a polynomial time. In this work, the computational power of tissue P systems with cell separation is investigated. Specifically, a uniform family of tissue P systems with ce...     

带膜分裂和促进剂的通讯膜系统求解QSAT问题

膜计算是自然计算的一个分支,膜计算中所研究的模型均称为膜系统,而细胞间通讯是膜系统的一个重要特征。带膜分裂的通讯膜系统是一种分布式并行计算模型,可以在多项式时间内解决计算困难问题。文中将促进剂引入带膜分裂的类细胞型通讯膜系统,提出了膜系统的一种变型——带膜分裂和促进剂的通讯膜系统,其中,一个促进剂可以同时控制多条规则,而促进剂本身不参与该条规则的进化。文中研究了带膜分裂和促进剂的通讯膜系统的计算效率,证明该类膜系统在使用同向规则长度为2,每条规则中促进剂的个数最多为1时,可以在多项式时间内求解PSPACE完全问题(QSAT问题)的统一解。     

Solving HPP and SAT by P Systems with Active Membranes and Separation Rules

The P systems (or membrane systems) are a class of distributed parallel computing devices of a biochemical type, where membrane division is the frequently investigated way for obtaining an exponential working space in a linear time, and on this basis solving hard problems, typically NP-complete problems, in polynomial (often, linear) time. In this paper, using another way to obtain exponential working space – membrane separation, it was shown that Satisfiability Problem and Hamiltonian Path Problem can be deterministically solved in linear or polynomial time by a uniform family of P systems with separation rules, where separation rules are not changing labels, but polarizations are used. Some related open problems are mentioned.     

Membrane computing and complexity theory: A characterization of PSPACE

A P system is a natural computing model inspired by information processing in cells and cellular membranes. We show that confluent P systems with active membranes solve in polynomial time exactly the class of problems PSPACE. Consequently, these P systems prove to be equivalent (up to a polynomial time reduction) to the alternating Turing machine or the PRAM computer. Similar results were achieved also with other models of natural computation, such as DNA computing or genetic algorithms. Our result, together with the previous observations, suggests that the class PSPACE provides a tight upper bound on the computational potential of biological information processing models.     

Spiking neural P systems with neuron division and budding

Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons.In this work,the features of neuron division and neuron budding are introduced into the framework of spiking neural P systems,which are processes inspired by neural stem cell division. With neuron division and neuron budding,a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes.In this way,spiking neural P systems can efficie...     

The GPU on the simulation of cellular computing models

Membrane Computing is a discipline aiming to abstract formal computing models, called membrane systems or P systems, from the structure and functioning of the living cells as well as from the cooperation of cells in tissues, organs, and other higher order structures. This framework provides polynomial time solutions to NP-complete problems by trading space for time, and whose efficient simulation poses challenges in three different aspects: an intrinsic massively parallelism of P systems, an exponential computational workspace, and a non-intensive floating point nature. In this paper, we analyze the simulation of a family of recognizer P systems with active membranes that solves the Satisfiability problem in linear time on different instances of Graphics Processing Units (GPUs). For an efficient handling of the exponential workspace created by the P systems computation, we enable different data policies to increase memory bandwidth and exploit data locality through tiling and dynamic queues. Parallelism inherent to the target P system is also managed to demonstrate that GPUs offer a valid alternative for high-performance computing at a considerably lower cost. Furthermore, scalability is demonstrated on the way to the largest problem size we were able to run, and considering the new hardware generation from Nvidia, Fermi, for a total speed-up exceeding four orders of magnitude when running our simulations on the Tesla S2050 server.     

Efficient simulation of tissue-like P systems by transition cell-like P systems

In the framework of P systems, it is known that the construction of exponential number of objects in polynomial time is not enough to efficiently solve NP-complete problems. Nonetheless, it could be sufficient to create an exponential number of membranes in polynomial time. Working with P systems whose membrane structure does not increase in size, it is known that it is not possible to solve computationally hard problems (unless P = NP), basically due to the impossibility of constructing exponential number of membranes, in polynomial time, using only evolution, communication and dissolution rules. In this paper we show how a family of recognizer tissue P systems with symport/antiport rules which solves a decision problem can be efficiently simulated by a family of basic recognizer P systems solving the same problem. This simulation allows us to transfer the result about the limitations in computational power, from the model of basic cell-like P systems to this kind of tissue-like P systems.     

A polynomial alternative to unbounded environment for tissue P systems with cell division

The standard definition of tissue P systems includes a special alphabet whose elements are assumed to appear in the initial configuration of the system in an arbitrarily large number of copies. These objects reside in a distinguished place of the system, called the environment. Such potentially infinite supply of objects seems an unfair tool when designing efficient solutions to computationally hard problems in the framework of membrane computing, by performing a space–time trade-off. This paper deals with computational aspects of tissue P systems with cell division where there is no environment having the property mentioned above. Specifically, we prove that the polynomial complexity classes associated with tissue P systems with cell division and with or without environment are actually identical. As a consequence, we conclude that it is not necessary to have infinitely many copies of some objects in the initial configuration in order to solve NP–complete problems in an efficient way.     

P Systems with Mobile Membranes

P systems with active membranes are among the central ones in membrane computing, and they were shown to be both computationally universal (able to simulate Turing machines) and computationally efficient (able to solve hard problems in polynomial time). However, in all cases, these results were obtained by making use of several powerful features, such as membrane polarization, label changing, division of non-elementary membranes, priorities, or cooperative rules. This paper contributes to the research effort of introducing a class of P systems with active membranes having none of the features mentioned above, but still preserving the power and the efficiency. The additional feature we consider instead are the operations of endocytosis and exocytosis: moving a membrane inside a neighboring membrane, or outside the membrane where it is placed. We investigate the power and the efficiency of these systems (also using membrane division) by first proving that they can simulate (with a linear slowdown and without introducing non-determinism) rewriting P systems with 2-replication, for which the universality and the possibility of solving NP-complete problems in polynomial time are known. In this way, the universality and efficiency are also obtained for our systems. We also give a direct and simple proof for the universality result – without using division rules (the proof uses nine membranes, but we do not know whether this number can be decreased).     

Evaluating the SAT problem on P systems for different high-performance architectures

Membrane computing is an emergent research area studying the behavior of living cells to define bio-inspired computing devices, also called P systems. Such devices provide polynomial time solutions to NP-complete problems by trading time for space. The efficient simulation of P systems poses three major challenging issues: an intrinsic massive parallelism of P systems, an exponential computational workspace, and a non-intensive floating point nature. This paper analyzes the simulation of a family of recognizer P systems with active membranes that solves the satisfiability problem in linear time on three different architectures: a shared memory multiprocessor, a distributed memory system, and a manycore graphics processing unit (GPU). For an efficient handling of the exponential workspace created by the P systems computation, we enable different data policies on those architectures to increase memory bandwidth and exploit data locality through tiling. Parallelism inherent to the target P system is also managed on each architecture to demonstrate that GPUs offer a valid alternative for high-performance computing at a considerably lower cost. Our results lead to execution time improvements exceeding 310 \(\times \) and 78 \(\times \) , respectively, for a much cheaper high-performance alternative.     

Further remark on P systems with active membranes and two polarizations

P systems (or membrane systems) are a class of distributed parallel computing devices of a biochemical type. In this paper, some restrictions on the general form of the developing rules are considered, under which it is still possible to solve NP-complete problems. We present an algorithm for deterministically deciding SAT in linear time by P systems with active membranes using two polarizations and rules of restricted versions of types (a), (c), (e). The result obtained in this paper answered an open problem proposed by Alhazov and Freund in the aspect of computing efficiency.     

Root mean square gain of discrete-time switched linear systems under dwell time constraints

This paper deals with discrete-time switched linear systems and considers the problem of computing an upper bound to the dwell time ensuring a pre-specified root mean square (RMS) gain. As a natural consequence of treating general systems of this class in terms of the order and the number of subsystems, only sufficient conditions are worked out. They depend on the complete separation of the stabilizing and anti-stabilizing solutions of the algebraic Riccati equation associated to each subsystem. Moreover, as positive features, it is shown that the dwell time preserving the specification can be calculated through linear matrix inequalities (LMIs) and line search, being thus numerically solvable in polynomial time, and this allows the treatment of stable switched linear systems which do not admit a common Lyapunov function. The case of a guaranteed RMS gain for arbitrary switching signals is also addressed. A simple academic example constituted by three subsystems of third order is included for illustration.     

A Linear–time Tissue P System Based Solution for the 3–coloring Problem

In the literature, several examples of the efficiency of cell-like P systems regarding the solution of NP-complete problems in polynomial time can be found (obviously, trading space for time). Recently, different new models of tissue-like P systems have received important attention from the scientific community. In this paper we present a linear-time solution to an NP-complete problem from graph theory, the 3-coloring problem, and we discuss the suitability of tissue-like P systems as a framework to address the efficient solution to intractable problems.     

Further remarks on P systems with active membranes, separation, merging, and release rules

The P systems are a class of distributed parallel computing devices of a biochemical type. In this note, we show that by using membrane separation to obtain exponential workspace, SAT problem can be solved in linear time in a uniform and confluent way by active P systems without polarizations. This improves some results already obtained by A. Alhazov, T.-O. Isdorj. A universality result related to membrane separation is also obtained.Dedicated to Prof. Zhang Kemin for his 70th birthday     

The computational power of cell division in P systems: Beating down parallel computers?

We study the computational power of cell division operations in the formalframework of P systems, a mathematical model of cell-like membrane structure with regulated transport of objects (molecules) through membranes. We show that a uniformfamily of P systems with active membranes and2-division is able to solve the well-known PSPACE-complete problem QBF inlinear time. This result implies that such a family of P systems modelling celldivision is at least as powerful as so-called Second Machine Class computers. The Second Machine Class, containing most of the fundamental parallelcomputer models such as parallel RAM machines of types SIMD and MIMD, vector machinesand others, is characterized by using an exponential amount of resources(processing units) with respect to the computing time.     

Solving two-dimensional cutting stock problem via a DNA computing algorithm

Two-dimensional cutting stock problem (TDCSP) is a well-known combinatorial optimization problem in which a given set of two-dimensional small pieces with different shapes should be cut from a given main board so that the demand of each small piece is satisfied and the total waste is minimized. Since TDCSP is an NP-complete problem, it is unsolvable in polynomial time on electronic computers. However, using the structure of DNA molecules, DNA computing algorithms are capable to solve NP-complete problems in polynomial time. In this paper, a DNA computing algorithm based on the sticker model is presented to find the optimal solution to TDCSP. It is proved that the time complexity of this algorithm on DNA computers is polynomial considering the number of small pieces and the length and width of the main board.

     

利用结构信息的故障诊断方法

基于模型的故障诊断方法是重要的故障诊断方法之一,该方法主要的问题就是如何求得所有的诊断．该文利用系统的结构信息,给出了求极小冲突集的一个算法,证明了算法的正确性,分析了算法的复杂性;然后介绍了如何由极小冲突求得诊断．其次,还给出了利用结构信息直接求诊断的一个算法,证明了其正确性．最后与相关工作进行了比较．该文给出的算法,对于一些特殊结构的系统可在多项式时间内结束．     

Using symmetries in the eigenvalue method for polynomial systems

One way of solving polynomial systems of equations is by computing a Gröbner basis, setting up an eigenvalue problem and then computing the eigenvalues numerically. This so-called eigenvalue method is an excellent bridge between symbolic and numeric computation, enabling the solution of larger systems than with purely symbolic methods. We investigate the case that the system of polynomial equations has symmetries. For systems with symmetry, some matrices in the eigenvalue method turn out to have special structure. The exploitation of this special structure is the aim of this paper. For theoretical development we make use of SAGBI bases of invariant rings. Examples from applications illustrate our new approach.     

基于距离度量的多样性图排序方法

有效结合查询相关性和多样性的扩展相关性是多样性图排序问题的一种优化目标.基于扩展相关性的多样性图排序可建模为一个子模函数优化问题,贪心子模优化算法可近似求解该问题.然而,扩展相关性不能直接度量节点间的不相似性.子模优化算法是串行算法不能充分利用诸如Spark等集群计算平台有效提高算法效率.针对这些问题,本文提出一种描述节点间不相似性的距离度量.基于此距离度量,将多样性图排序问题建模为一个在查询相关节点集上构造的带权完全图的最大和k-dispersion优化问题.提出了求解该问题的多项式时间2-近似算法.鉴于不同节点对的距离度量计算是相互独立的,进一步地提出了基于MapReduce编程模型的并行化多样性图排序算法.最后,在真实图数据集上验证了本文提出算法的高效性和有效性.