Size of ordered binary decision diagrams representing threshold functions

An ordered binary decision diagram (OBDD) is a graph representation of a Boolean function. In this paper, the size of ordered binary decision diagrams representing threshold functions is discussed. We consider two cases: the case when a variable ordering is given and the case when it is adaptively chosen. We show 1) O(2^n/2) upper bound for both cases, 2) Ω(2^n/2) lower bound for the former case and 3) Ω(n2^√n/2) lower bound for the latter case. We also show some relations between the variable ordering and the size of OBDDs representing threshold functions.     

New bounds for multi-label interval routing

Interval routing (IR) is a space-efficient routing method for computer networks. For longest routing path analysis, researchers have focused on lower bounds for many years. For any n-node graph G of diameter D, there exists an upper bound of 2D for IR using one or more labels, and an upper bound of for IR using or more labels. We present two upper bounds in the first part of the paper. We show that for every integer i>0, every n-node graph of diameter D has a k-dominating set of size for . This result implies a new upper bound of for IR using or more labels, where i is any positive integer constant. We apply the result by Kutten and Peleg [8] to achieve an upper bound of (1+)D for IR using O(n/D) or more labels, where is any constant in (0,1). The second part of the paper offers some lower bounds for planar graphs. For any M-label interval routing scheme (M-IRS), where , we derive a lower bound of [(2M+1)/(2M)]D−1 on the longest path for , and a lower bound of [(2(1+δ)M+1)/(2(1+δ)M)]D, where δ(0,1], for . The latter result implies a lower bound of on the number of labels needed to achieve optimality.     

On the complexity of finding a local maximum of functions on discrete planar subsets

We study how many values of an unknown integer-valued function f one needs to know in order to find a local maximum of f. We consider functions defined on finite subsets of discrete plane. We prove upper bounds for functions defined on rectangles and present lower bounds for functions defined on arbitrary domains in terms of the size of the domain and the size of its border.     

On fast planning of suboptimal paths amidst polygonal obstacles in plane

The problem of planning a path for a point robot from a source point s to a destination point d so as to avoid a set of polygonal obstacles in plane is considered. Using well-known methods, a shortest path from s to d can be computed with a time complexity of O(n²) where n is the total number of obstacle vertices. The focus here is in

1. (a) planning paths faster at the expense of setting for suboptimal path lengths and

2. (b) performance analysis of simple and/or well-known suboptimal methods.

A method that enables a hierarchical implementation of any path planning algorithm with no increase in the worst-case time complexity, is presented; this implementation enables fast planning of simple paths. Then methods are presented based on the Voronoi diagrams, trapezoidal decomposition and triangulation, which compute (suboptimal) paths in O(n√log n) time with the preprocessing costs of O(n log n), O(n²) and O(n log n), respectively. Using existing navigational algorithms for unknown terrains, algorithms that run in O(n log n) time (after preprocessing) and yield suboptimal paths, are presented. For all these algorithms, upper bounds on the path lengths are estimated in terms of the shortest of the obstacles, etc.     

Efficient algorithms for single- and two-layer linear placement of parallel graphs

This paper outlines an algorithm for optimum linear ordering (OLO) of a weighted parallel graph with O(n log k) worst-case time complexity, and O(n + k log(n/k) log k) expected-case time complexity, where n is the total number of nodes and k is the number of chains in the parallel graph. Next, the two-layer OLO problem is considered, where the goal is to place the nodes linearly in two routing layers minimizing the total wire length. The two-layer problem is shown to subsume the maxcut problem and a befitting heuristic algorithm is proposed. Experimental results on randomly generated samples show that the heuristic algorithm runs very fast and outputs optimum solutions in more than 90% instances.     

Detecting the epistatic structure of generalized embedded landscape

Working under the premise that most optimizable functions are of bounded epistasis, this paper addresses the problem of discovering the linkage structure of a black-box function with a domain of arbitrary-cardinality under the assumption of bounded epistasis. To model functions of bounded epistasis, we develop a generalization of the mathematical model of “embedded landscapes” over domains of cardinality M. We then generalize the Walsh transform as a discrete Fourier transform, and develop algorithms for linkage learning of epistatically bounded GELs. We propose Generalized Embedding Theorem that models the relationship between the underlying decomposable structure of GEL and its Fourier coefficients. We give a deterministic algorithm to exactly calculate the Fourier coefficients of GEL with bounded epistasis. Complexity analysis shows that the epistatic structure of epistatically bounded GEL can be obtained after a polynomial number of function evaluations. Finally, an example experiment of the algorithm is presented.     

On the power of alternation on reversal-bounded alternating Turing machines with a restriction

Whether or not there is a difference of the power among alternating Turing machines with a bounded number of alternations is one of the most important problems in the field of computer science. This paper presents the following result: Let R(n) be a space and reversal constructible function. Then, for any k 1, we obtain that the class of languages accepted by off-line 1-tape rσ_k machines running in reversal O(R(n)) is equal to the class of languages accepted by off-line 1-tape σ₁ machines running in reversal O(R(n)). An off-line 1-tape σ_k machine M is called an off-line 1-tape rσ_k machine if M always limits the non-blank part of the work-tape to at most O(R(n) log n) when making an alternation between universal and existential states during the computation.     

Reaching a goal with directional uncertainty

We study two problems related to planar motion planning for robots with imperfect control, where, if the robot starts a linear movement in a certain commanded direction, we only know that its actual movement will be confined in a cone of angle centered around the specified direction.

First, we consider a single goal region, namely the “region at infinity”, and a set of polygonal obstacles, modeled as a set S of n line segments. We are interested in the region from where we can reach infinity with a directional uncertainty of . We prove that the maximum complexity of is O(n/⁵). Second, we consider a collection of k polygonal goal regions of total complexity m, but without any obstacles. Here we prove an O(k³m) bound on the complexity of the region from where we can reach a goal region with a directional uncertainty of . For both situations we also prove lower bounds on the maximum complexity, and we give efficient algorithms for computing the regions.     

Optimal bounds for the approximation of boolean functions and some applications

This paper presents an optimal bound on the Shannon function L(n,m,) that gives the worstcase circuit-size complexity to approximate, within an approximation degree at least , partial boolean functions having n inputs and domain size m. That is

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. Our bound applies to any partial boolean function and any approximation degree, and thus completes the study of boolean function approximation introduced by Pippenger (1977).

Our results give an upper bound for the hardness function h(ƒ), introduced by Nisan and Wigderson (1994), which denotes the minimum value l for which there exists a circuit of size at most l that approximates a boolean function ƒ with degree at least 1/l. Indeed, if H(n) denotes the maximum hardness value achieved by boolean functions with n inputs, we prove that for almost every nH(n)n/3 + n² + O(1). The exponent n/3 in the above inequality implies that no family of boolean functions exists which has ‘full’ hardness. This fact establishes connections with Allender and Strauss' (1994) work that explores the structure of BPP.

Finally, we show that for almost every n and for almost every boolean function ƒ of n inputs we have h(ƒ)2^{n/3−2 log n}. The contribution in the proof of the upper bound for L(n, m, ) can be viewed as a set of technical results that globally show how boolean linear operators are ‘well’ distributed on the class of 4-regular domains. This property is then applied to approximate partial boolean functions on general domains using a suitable composition of boolean linear operators.     

抗合谋攻击能力可调的有状态组密钥更新协议

逻辑密钥分层（LKH）协议已经被证明在抗完全合谋攻击时,它通信开销的下界是O（log n）,但是在一些资源受限或者商业应用场景中,用户仍然要求通信开销低于O（log n）。虽然,有状态的完全排外子树（SECS）协议具有常量通信开销的特性,却只能抵抗单用户攻击。考虑用户愿意牺牲一定安全性来降低通信开销的情况,利用LKH协议的完全抗合谋攻击特性和SECS协议具有常量通信开销的优势,设计并实现了一种混合的组密钥更新协议（H-SECS）。H-SECS协议根据应用场景的安全级别来配置子组数目,在通信开销和抗合谋攻击能力之间作一个最优的权衡。理论分析及仿真实验表明,与LKH协议和SECS协议相比,H-SECS协议的通信开销可以在O（1）和O（log n）区间进行调控。     

Efficient enumeration of all minimal separators in a graph

This paper presents an efficient algorithm for enumerating all minimal a-b separators separating given non-adjacent vertices a and b in an undirected connected simple graph G = (V, E), Our algorithm requires O(n³R_ab) time, which improves the known result of O(n⁴R_ab) time for solving this problem, where ¦V¦= n and R_ab is the number of minimal a-b separators. The algorithm can be generalized for enumerating all minimal A-B separators that separate non-adjacent vertex sets A, B < V, and it requires O(n²(n − n_A − n_b)R_AB) time in this case, where n_a = ¦A¦, n_B = ¦B¦ and r_AB is the number of all minimal A−B separators. Using the algorithm above as a routine, an efficient algorithm for enumerating all minimal separators of G separating G into at least two connected components is constructed. The algorithm runs in time O(n³R⁺_Σ + n⁴R_Σ), which improves the known result of O(n⁶R_Σ) time, where R_σ is the number of all minimal separators of G and R_ΣR⁺_Σ = ∑_1i, v_j) ER_vivj n − 1)/2 − m)R_Σ. Efficient parallelization of these algorithms is also discussed. It is shown that the first algorithm requires at most O((n/log n)R_ab) time and the second one runs in time O((n/log n)R⁺_Σ+n log nR_Σ) on a CREW PRAM with O(n³) processors.     

Computational complexity of distributed detection problems with information constraints

For a system consisting of a set of sensors S = {S₁, S₂, …, S_m} and a set of objects O = {O₁, O₂, …, O_n}, there are information constraints given by a relation R S × O such that (S_i, O_j) R if and only if S_i is capable of detecting O_j. Each (S_i, O_j) R is assigned a confidence factor (a positive real number) which is either explicitly given or can be efficiently computed. Given that a subset of sensors have detected obstacles, the detection problem is to identify a subset H O with the maximum confidence value. The computational complexity of the detection problem, which depends on the nature of the confidence factor and the information constraints, is the main focus of this paper. This problem exhibits a myriad of complexity levels ranging from a worst-case exponential (in n) lower bound in a general case to an O(m + n) time solvability. We show that the following simple versions of a detection problem are computationally intractable: (a) deterministic formulation, where confidence factors are either 0 or 1; (b) uniform formulation where (S_i, O_j) R, for all S_i S, O_j O; (c) decomposable systems under multiplication operation. We then show that the following versions are solvable in polynomial (in n) time: (a) single object detection; (b) probabilistically independent detection; (c) decomposable systems under additive and nonfractional multiplicative measures; and (d) matroid systems.     

On the Incompressibility of Monotone DNFs

We prove optimal lower bounds for multilinear circuits and for monotone circuits with bounded depth. These lower bounds state that, in order to compute certain functions, these circuits need exactly as many OR gates as the respective DNFs. The proofs exploit a property of the functions that is based solely on prime implicant structure. Due to this feature, the lower bounds proved also hold for approximations of the considered functions that are similar to slice functions. Known lower bound arguments cannot handle these kinds of approximations. In order to show limitations of our approach, we prove that cliques of size n - 1 can be detected in a graph with n vertices by monotone formulas with O(log n) OR gates. Our lower bound for multilinear circuits improves a lower bound due to Borodin, Razborov and Smolensky for nondeterministic read-once branching programs computing the clique function.     

Optimizing Rekeying Cost for Contributory Group Key Agreement Schemes

Although a contributory group key agreement is a promising solution to achieve access control in collaborative and dynamic group applications, the existing schemes have not achieved the performance lower bound in terms of time, communication, and computation costs. In this paper, we propose a contributory group key agreement that achieves the performance lower bound by utilizing a novel logical key tree structure, called PFMH, and the concept of phantom user position. In particular, the proposed scheme only needs O(1) rounds of the two-party Diffie-Hellman (DH) upon any single-user join event and O(log n) rounds of the two-party DH upon any single-user leave event. Both the theoretical bound analysis and simulation show that the proposed scheme achieves a lower rekeying cost than the existing tree-based contributory group key agreement schemes.     

An asymptotic theory for recurrence relations based on minimization and maximization

We derive asymptotic approximations for the sequence f(n) defined recursively by f(n)=min_1j<n {f(j)+f(n−j)}+g(n), when the asymptotic behavior of g(n) is known. Our tools are general enough and applicable to another sequence F(n)=max_1j<n {F(j)+F(n−j)+min{g(j),g(n−j)}}, also frequently encountered in divide-and-conquer problems. Applications of our results to algorithms, group testing, dichotomous search, etc. are discussed.     

基于图勾勒的图链路预测方法

针对已有链路预测算法复杂度高,不适于在大规模图上进行链接预测的问题,本文基于图勾勒近似技术对已有链路预测方法进行优化,提出了基于图勾勒的链路预测方法。该方法将链路预测算法的计算复杂度由O（n³）降低至O（n²k²log²n）。为进一步提高链接预测效率,给出了基于Spark的并行化链路预测实现方法。在真实图数据集上进行测试,实验结果表明本文方法在保证链接预测精度的前提下,可有效提升算法效率。     

Parallel arithmetic expression evaluation on reconfigurable meshes

In this paper we present an O(log n) time parallel algorithm for arithmetic expression evaluation, on an n × n processor array with reconfigurable bus system, where n is the sum of the number of operators and constants in the expression. The basic technique involved here is leaves-cutting (rake operation), as in the case of PRAM model algorithms available in the literature for this problem. The input to our algorithm is assumed to be the binary tree associated with a given expression (also known as expression tree with n number of nodes). Our algorithm is faster compared to the previous best time for expression evaluation on mesh connected computers which is O(√n).     

Order-configuration functions: Mathematical characterizations and applications to digital signal and image processing

We call a function f in n variables an order-configuration function if for any x₁,…, x_n such that x_i1 … x_in we have f(x₁,…, x_n) = x_t, where t is determined by the n-tuple (i₁,…, i_n) corresponding to that ordering. Equivalently, it is a function built as a minimum of maxima, or a maximum of minima. Well-known examples are the minimum, the maximum, the median, and more generally rank functions, or the composition of rank functions. Such types of functions are often used in nonlinear processing of digital signals or images (for example in the median or separable median filter, min-max filters, rank filters, etc.). In this paper we study the mathematical properties of order-configuration functions and of a wider class of functions that we call order-subconfiguration functions. We give several characterization theorems for them. We show through various examples how our concepts can be used in the design of digital signal filters or image transformations based on order-configuration functions.     

Optimal and nearly optimal algorithms for approximating polynomial zeros

We substantially improve the known algorithms for approximating all the complex zeros of an n^th degree polynomial p(x). Our new algorithms save both Boolean and arithmetic sequential time, versus the previous best algorithms of Schönhage [1], Pan [2], and Neff and Reif [3]. In parallel (NC) implementation, we dramatically decrease the number of processors, versus the parallel algorithm of Neff [4], which was the only NC algorithm known for this problem so far. Specifically, under the simple normalization assumption that the variable x has been scaled so as to confine the zeros of p(x) to the unit disc x : |x| ≤ 1, our algorithms (which promise to be practically effective) approximate all the zeros of p(x) within the absolute error bound 2^−b, by using order of n arithmetic operations and order of (b + n)n² Boolean (bitwise) operations (in both cases up to within polylogarithmic factors). The algorithms allow their optimal (work preserving) NC parallelization, so that they can be implemented by using polylogarithmic time and the orders of n arithmetic processors or (b + n)n² Boolean processors. All the cited bounds on the computational complexity are within polylogarithmic factors from the optimum (in terms of n and b) under both arithmetic and Boolean models of computation (in the Boolean case, under the additional (realistic) assumption that n = O(b)).     

Decision tree approximations of Boolean functions

Decision trees are popular representations of Boolean functions. We show that, given an alternative representation of a Boolean function f, say as a read-once branching program, one can find a decision tree T which approximates f to any desired amount of accuracy. Moreover, the size of the decision tree is at most that of the smallest decision tree which can represent f and this construction can be obtained in quasi-polynomial time. We also extend this result to the case where one has access only to a source of random evaluations of the Boolean function f instead of a complete representation. In this case, we show that a similar approximation can be obtained with any specified amount of confidence (as opposed to the absolute certainty of the former case.) This latter result implies proper PAC-learnability of decision trees under the uniform distribution without using membership queries.