A family of Hermite interpolants by bisection algorithms

A two point subdivision scheme with two parameters is proposed to draw curves corresponding to functions that satisfy Hermite conditions on [a, b]. We build two functionsf andf ¹ on dyadic numbers and for some values of the parameters,f is in ¹ withf ¹=f. Examples are provided which show how different the curves can be.     

Tight bounds for minimax grid matching with applications to the average case analysis of algorithms

The minimax grid matching problem is a fundamental combinatorial problem associated with the average case analysis of algorithms. The problem has arisen in a number of interesting and seemingly unrelated areas, including wafer-scale integration of systolic arrays, two-dimensional discrepancy problems, and testing pseudorandom number generators. However, the minimax grid matching problem is best known for its application to the maximum up-right matching problem. The maximum up-right matching problem was originally defined by Karp, Luby and Marchetti-Spaccamela in association with algorithms for 2-dimensional bin packing. More recently, the up-right matching problem has arisen in the average case analysis of on-line algorithms for 1-dimen-sional bin packing and dynamic allocation.In this paper, we solve both the minimax grid matching problem and the maximum up-right matching problem. As a direct result, we obtain tight upper bounds on the average case behavior of the best algorithms known for 2-dimensional bin packing, 1-dimensional on-line bin packing and on-line dynamic allocation. The results also solve a long-open question in mathematical statistics.This research was supported by Air Force Contracts AFOSR-82-0326 and AFOSR-86-0078, NSF Grant 8120790, and DARPA contract N00014-80-C-0326. In addition, Tom Leighton was supported by an NSF Presidential Young Investigator Award with matching funds from Xerox and IBM.     

Rudiments of an average case complexity theory for piecewise-linear path following algorithms

We examine the efficiency of PL path following algorithms in followingF _T ^-1 (0), whereF _T is the PL approximation, induced by the simplicial triangulationT, to a mapf: ^{n n-1}. In particular, we consider the problem of determining an upper bound on the expected number of pivots made per unit length off ^–1(0) that is approximated. We show that if the sizes of the simplices ofT are sufficiently small, where sufficiently small is an explicitly given quantity dependent on measurements of how nicef is, then the average directional density ofT, as introduced by Todd, really does give a good approximation to the expected number of pivots made, confirming what researchers have believed on intuitive grounds for a decade. Because what constitutes sufficiently small is a precisely given quantity, i.e., non-asymptotic, we are able to provide some rigorous justification for the claim that the expected number of pivots grows only polynomially inn, the number of variables.Several other issues are also examined.Research supported by an NSF Mathematical Sciences Postdoctoral Research Fellowship. This research was performed while the author was a member of the Mathematical Sciences Research Institute, Berkeley, California.     

Graph operations that are good for greedoids

S is a local maximum stable set of a graph G, and we write S∈Ψ(G), if the set S is a maximum stable set of the subgraph induced by S∪N(S), where N(S) is the neighborhood of S. In Levit and Mandrescu (2002) [5] we have proved that Ψ(G) is a greedoid for every forest G. The cases of bipartite graphs and triangle-free graphs were analyzed in Levit and Mandrescu (2003) [6] and Levit and Mandrescu (2007) [7] respectively.In this paper we give necessary and sufficient conditions for Ψ(G) to form a greedoid, where G is:

(a)

the disjoint union of a family of graphs;

(b)

the Zykov sum of a family of graphs;

(c)

the corona X°{H₁,H₂,…,H_n} obtained by joining each vertex x of a graph X to all the vertices of a graph H_x.

     

Graph algorithms on a tree-structured parallel computer

Parallel algorithms for some graph-theoretic problems on a tree-structured computer are presented. In particular, ifp denotes the number of processing elements, algorithms that run inO(n ²/p) time for finding connected components, transitive closure and the minimum spanning tree of an undirected graph withn vertices are obtained.     

最大割问题和最大平分割问题基于半定规划松弛的近似算法

考虑每条边具有非负权重的无向图, 最大割问题要求将顶点集划分为两个集合使得它们之间的边的权重之和最大. 当最大割问题半定规划松弛的最优解落到二维空间时, Goemans将近似比从0.87856...改进为0.88456. 依赖于半定规划松弛的目标值与总权和的比值的曲线, 此曲线的最低点为0.88456, 当半定规划松弛的目标值与总权和的比值在0.5到0.9044之间时, 利用Gegenbauer多项式舍入技巧, 改进了Zwick的近似比曲线. 进一步, 考虑最大割问题的重要变形------最大平分割问题, 在此问题中增加了划分的两部分的点数相等的要求. 同样考虑了最大平分割问题半定规划松弛的最优解落到二维空间的情形, 并利用前述的Gegenbauer多项式舍入技巧得到0.7091-近似算法.     

How good are convex hull algorithms?

A convex polytope P can be specified in two ways: as the convex hull of the vertex set V of P, or as the intersection of the set H of its facet-inducing halfspaces. The vertex enumeration problem is to compute V from H>. The facet enumeration problem is to compute H from V. These two problems are essentially equivalent under point/hyperplane duality. They are among the central computational problems in the theory of polytopes. It is open whether they can be solved in time polynomial in |H| + |V| and the dimension. In this paper we consider the main known classes of algorithms for solving these problems. We argue that they all have at least one of two weaknesses: inability to deal well with “degeneracies”, or, inability to control the sizes of intermediate results. We then introduce families of polytopes that exercise those weaknesses. Roughly speaking, fat-lattice or intricate polytopes cause algorithms with bad degeneracy handling to perform badly; dwarfed polytopes cause algorithms with bad intermediate size control to perform badly. We also present computational experience with trying to solve these problem on these hard polytopes, using various implementations of the main algorithms.     

On some algorithms for limiting average Markov decision processes

We consider limiting average Markov decision processes (MDP) with finite state and action spaces. We propose some algorithms to determine optimal strategies for deterministic and general MDPs. These algorithms are based on graph theory and the construction of levels in some aggregated MDP.     

Graph coloring in a class of parallel local algorithms

A way of improving the performance of a distributed algorithm is rendering a coloring strategy into an algorithm known as efficient in the nondistributed case. In this paper it is shown that certain sequential coloring algorithm heuristics like largest-first (LF), smallest-last (SL), and saturation largest-first (SLF), as applied to some classes of graphs and to special cases of vertex coloring in distributed algorithms, produce an optimal or near-optimal coloring.     

LP and SDP branch-and-cut algorithms for the minimum graph bisection problem: a computational comparison

While semidefinite relaxations are known to deliver good approximations for combinatorial optimization problems like graph bisection, their practical scope is mostly associated with small dense instances. For large sparse instances, cutting plane techniques are considered the method of choice. These are also applicable for semidefinite relaxations via the spectral bundle method, which allows to exploit structural properties like sparsity. In order to evaluate the relative strengths of linear and semidefinite approaches for large sparse instances, we set up a common branch-and-cut framework for linear and semidefinite relaxations of the minimum graph bisection problem. It incorporates separation algorithms for valid inequalities of the bisection cut polytope described in a recent study by the authors. While the problem specific cuts help to strengthen the linear relaxation significantly, the semidefinite bound profits much more from separating the cycle inequalities of the cut polytope on a slightly enlarged support. Extensive numerical experiments show that this semidefinite branch-and-cut approach without problem specific cuts is a superior choice to the classical simplex approach exploiting bisection specific inequalities on a clear majority of our large sparse test instances from VLSI design and numerical optimization.     

Convergence behavior of interior-point algorithms

We show that most interior-point algorithms for linear programming generate a solution sequence in which every limit point satisfies the strict complementarity condition. These algorithms include all path-following algorithms and some potential reduction algorithms. The result also holds for the monotone complementarity problem if a strict complementarity solution exists. In general, the limit point is a solution that maximizes the number of its nonzero components among all solutions.Research supported in part by NSF Grant DDM-8922636, the Iowa Business School Summer Grant, and the Interdisciplinary Research Grant of the University of Iowa Center for Advanced Studies.     

Are linear algorithms always good for linear problems?

We exhibit linear problems for which every linear algorithm has infinite error, and show a (mildly) nonlinear algorithm with finite error. The error of this nonlinear algorithm can be arbitrarily small if appropriate information is used. We illustrate these examples by the inversion of a finite Laplace transform, a problem arising in remote sensing.     

Asymptotical good behavior on inequalities with completely approximate K-T concept

We characterize a wide class of regular convex functionals that are asymptotically well behaved on a convex set given by (infinite) inequalities, namely, those restricted functions whose stationary sequences (bounded or not) are minimizing ones. After showing the equivalence with the Kuhn-Tucker type stationarity, we prove that the class of such functions remains unchanged when the Kuhn-Tucker system is completely relaxed. This allows us to proceed for enlarging the scope of convergence of certain penalty (exterior as well as interior) methods including a new exterior penalization for infinite inequalities.     

Anomalous behavior in bin packing algorithms

In the classical bin packing problem one is given a list of items and asked to pack them into the fewest possible unit-sized bins. Given two lists, L₁ and L₂, where L₂ is derived from L₁ by deleting some elements of L₁ and/or reducing the size of some elements of L₁, one might hope that an approximation algorithm would use no more bins to pack L₂ than it uses to pack L₁. Johnson and Graham have given examples showing that First-Fit and First-Fit Decreasing can actually use more bins to pack L₂ than L₁. Graham has also studied this type of behavior among multiprocessor scheduling algorithms. In the present paper we extend this study of anomalous behavior to a broad class of approximation algorithms for bin packing. To do this we introduce a technique which allows one to characterize the monotonic/anomalous behavior of any algorithm in a large, natural class. We then derive upper and lower bounds on the anomalous behavior of the algorithms which are anomalous and provide conditions under which a normally nonmonotonic algorithm becomes monotonic.     

The residual based interactive stochastic gradient algorithms for controlled moving average models

This paper derives a residual based interactive stochastic gradient (ISG) parameter estimation algorithm for controlled moving average (CMA) models and studied the performance of the residual based ISG algorithm under weaker conditions on statistical properties of the noise. Compared with the residual based extended stochastic gradient algorithm for identifying CMA models, the proposed ISG algorithm can give highly accurate parameter estimates by the simulation example.     

On average case errors in numerical analysis

The error of a numerical method may be much smaller for most instances than for the worst case. Also, two numerical methods may have the same maximal error although one of them usually is much better than the other. Such statements can be made precise by concepts from average case analysis. We give some examples where such an average case analysis seems to be more sensible than a worst case analysis.     

An improvement of fixed point algorithms by using a good triangulation

We consider measures for triangulations ofR ⁿ. A new measure is introduced based on the ratio of the length of the sides and the content of the subsimplices of the triangulation. In a subclass of triangulations, which is appropriate for computing fixed points using simplicial subdivisions, the optimal one according to this measure is calculated and some of its properties are given. It is proved that for the average directional density this triangulation is optimal (within the subclass) asn goes to infinity. Furthermore, we compare the measures of the optimal triangulation with those of other triangulations. We also propose a new triangulation of the affine hull of the unit simplex. Finally, we report some computational experience that confirms the theoretical results.     

Limiting behavior of generalized delayed average of random sequence

Let (ξ _n ) _n∈N be a sequence of arbitrarily dependent random variables, and let \(T_{a_n ,k_n }\) be delayed sums. By virtue of the notion of asymptotic delayed log-likelihood ratio and Laplace transformation, the almost sure limiting behavior of the generalized delayed averages \(\frac{1} {{k_n }}T_{a_n ,k_n }\) is investigated, of which the results generalize and improve some work of Liu.     

On the asymptotic behavior of some algorithms

A simple approach is presented to study the asymptotic behavior of some algorithms with an underlying tree structure. It is shown that some asymptotic oscillating behaviors can be precisely analyzed without resorting to complex analysis techniques as it is usually done in this context. A new explicit representation of periodic functions involved is obtained at the same time. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2005