Abstract. The goal of this paper is to point out that analyses of parallelism in computational problems have practical implications even when multiprocessor machines are not available. This is true because, in many cases, a good parallel algorithm for one problem may turn out to be useful for designing an efficient serial algorithm for another problem. A d ~ eframework d for cases like this is presented. Particular cases, which are discussed in this paper, provide motivation for examining parallelism in sorting, selection, minimum-spanning-tree, shortest route, max-flow, and matrix multiplication problems, as well as in scheduling and locational problems.

We investigate two strategies for reducing the clock period of a two-phase, levelclocked circuit: clock tuning, which adjusts the waveforms that clock the circuit, and retiming, which relocates circuit latches. These methods can be used to convert a circuit with edge-triggered latches into a faster level-clocked one. We model a two-phase circuit as a graph whose vertex set V is a collection of combinational logic blocks, and whose edge set E is a set of interconnections. Each interconnection passes through 0 or more latches, where each latch is clocked by one of two periodic, nonoverlapping waveforms, or phases. We give efficient polynomial-time algorithms for problems involving the timing verification and optimization of two-phase circuitry. Included are algorithms for ffl verifyi...

Abstract. We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,...), and from the interior of the region (texture, homogeneity,. We describe two polynomial-time digraph algorithms for finding the global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 × 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization. 1.

We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis.

We propose a new form of energy functional for the segmentation of regions in images, and an efficient method for finding its global optima. The energy can have contributions from both the region and its boundary, thus combining the best features of region- and boundary-based approaches to segmentation. By transforming the region energy into a boundary energy, we can treat both contributions on an equal footing, and solve the global optimization problem as a minimum mean weight cycle problem on a directed graph. The simple, polynomial-time algorithm requires no initialization and is highly parallelizable.

Let D = (V, E) be a digraph with n vertices and m arcs. For each e E E there is an associated cost ce and a transit time te; Ce can be arbitrary, but we require t to be a non-negative integer. The cost to time ratio of a cycle C is X(C) = 3 ec ceCeec t. Let E ' c E denote the set of arcs e with te> 0, let T = max{tv: (u, v) E} for each vertex u, and let T = uev T. We give a new algorithm for finding a cycle C with the minimum cost to time ratio X(C). The algorithm's (T(m + n log n)) running time is dominated by O(T) shortest paths calculations on a digraph with non-negative arc lengths. Further, we consider early termination of the algorithm and a faster O(Tm) algorithm in case E- E ' is acyclic, i.e., in case each cycle has a strictly positive transit time, which gives an O(n 2) algorithm for a class of cyclic staffing problems considered by Bartholdi et al. The algorithm can be seen to be an extension of the O(nm) algorithm of Karp for the case in which t = 1 for all e E E, which is the problem of calculating a minimum mean cycle. Our algorithm can also be modified to solve the related parametric shortest paths problem in O(T(m + n log n)) time. © 1993 by John Wiley & Sons, Inc. 1.

In this paper, we study inverse optimization problems defined as follows. Let S denote the set of feasible solutions of an optimization problem P, let c be a specified cost vector, and x 0 be a given feasible solution. The solution x 0 may or may not be an optimal solution of P with respect to the cost vector c. The inverse optimization problem is to perturb the cost vector c to d so that x 0 is an optimal solution of P with respect to d and �d − c � p is minimum, where �d − c � p is some selected L p norm. In this paper, we consider the inverse linear programming problem under L 1 norm (where �d − c � p = ∑ i∈J w j�d j − c j�, with J denoting the index set of variables x j and w j denoting the weight of the variable j) and under L � norm (where �d −c � p = max j∈J �w j�d j −c j���. We prove the following results: (i) If the problem P is a linear programming problem, then its inverse problem under the L 1 as well as L � norm is also a linear programming problem. (ii) If the problem P is a shortest path, assignment or minimum cut problem, then its inverse problem under the L 1 norm and unit weights can be solved by solving a problem of the same kind. For the nonunit weight case, the inverse problem reduces to solving a minimum cost flow problem. (iii) If the problem P is a minimum cost flowproblem, then its inverse problem under the L 1 norm and unit weights reduces to solving a unit-capacity minimum cost flowproblem. For the nonunit weight case, the inverse problem reduces to solving a minimum cost flowproblem. (iv) If the problem P is a minimum cost flowproblem, then its inverse problem under the L � norm and unit weights reduces to solving a minimum mean cycle problem. For the nonunit weight case, the inverse problem reduces to solving a minimum cost-to-time ratio cycle problem. (v) If the problem P is polynomially solvable for linear cost functions, then inverse versions of P under the L 1 and L � norms are also polynomially solvable.

Asynchronous pipelining is a form of parallelism in which processors execute different loop tasks (loop statements) as opposed to different loop iterations. An asynchronous pipeline schedule for a loop is an assignment of loop tasks to processors, plus an order on instances of tasks assigned to the same processor. This variant of pipelining is particularly relevant in distributed memory systems (since pipeline control may be distributed across processors), but may also be used in shared memory systems. Accurate estimation of the execution time of a pipeline schedule is needed to determine if pipelining is appropriate for a loop, and to compare alternative schedules. Pipeline execution of n iterations of a loop requires time at most a + bn, for some constants a and b. The coefficient b is the iteration interval of the pipeline schedule, and is the primary measure of the performance of a schedule. The startup time a is a secondary performance measure. We generalize previous work on det...

2 Deterministic dynamic/periodic optimization problems arise naturally in various quantitative disciplines including Computer Science, Economics, and Operations Research. These periodic models may be applied to long-range

A symmetric scaling of a square matrix A #0 is a matrix of the form XAX- ' where X is a nonnegative, nonsingular, diagonal matrix having the same dimension of A. An asymmetric scaling of a rectangular matrix B # 0 is a matrix of the form XBY- ' where X and Y"are nonnegative, nonsingular, diagonal matrices having appropriate dimensions. We consider two objectives in selecting a symmetric scaling of a given matrix. The first is to select a scaling A ' of a given matrix A such that the maximal absolute value of the elements of A ' is lesser or equal that of any other corresponding scaling of A. The second is to select a scaling B ' of a given matrix B such that the maximal absolute value of ratios of nonzero elements of B ' is lesser or equal that of any other corresponding scaling of B. We also consider the problem of finding an optimal asymmetric scaling under the maximal ratio criterion (the maximal element criterion is, of course, trivial in this case). We show that these problems can be converted to parametric network problems which can be solved by corresponding algorithms.