J.R. Evans, E. Minieka, Optimization Algorithms for Networks and Graphs, second ed., Dekker, New York, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....WNet (n) work capacity values in our target environment. Because the work flows in a MW computation form a tree rooted at the master, and because we have limited our investigation to considering no more than one process hosted on each processor, e#cient algorithms like the Maximum Flow algorithm [22] exist for solving this problem. This approach can be used iteratively or in parallel to solve the flow rate problem for several candidate processes m, finding the one which is expected to deliver the maximum work flow, and hence the best expected application performance. In Chapter 3 we give the ....

Evans, J. R., and Minieka, E. Optimization Algorithms for Networks and Graphs, second ed. Marcel Dekker, Inc., 1992, ch. 5, pp. 178--233.

....work capacities in our target environment. Because the work flows in a master slave computation form a tree rooted at the master, and because we have limited our investigation to considering no more than one process hosted on each processor, efficient algorithms like the Maximum Flow algorithm [5] exist for solving this problem. This approach can be used to solve the flow rate problem for several candidate processes m, finding the one which is expected to deliver the maximum work flow, and hence the best expected application performance. Section 4 describes the implementation of one such ....

J. R. Evans and E. Minieka. Optimization Algorithms for Networks and Graphs, chapter 5, pages 178--233. Marcel Dekker, Inc., second edition, 1992.

....= 1 (C) 2.1) We omit the proof. 4 Proofs of several special cases of Theorem 2.1 have been published. Dowling [7] gives a proof for = GK n (Example I.6.7) along with a strong converse for that example. Proofs for G = R have appeared in the literature of networks with gains; cf. [14]. A detailed proof for #G = 2 in [18, Section 8A] is based on Equation (2.1) it adapts readily to the general commutative case. There is a dual representation by hyperplanes. In F N let x v be the coordinate corresponding to node v. Let h(e) be the hyperplane speci ed by x v = e; v; w)xw if ....

E. Minieka, \Optimization Algorithms for Networks and Graphs," Marcel Dekker, New York, 1978. MR 80a:90066.

....a cycle is found . if ( P.isCycle( findCycle( P ) return true; If a cycle is not found, backtrack by removing the last Edge object from the given Path and marking this Edge as being UNSEEN again . P.backtrack( E.status = UNSEEN; return false; 45. Refer to [Evan92] and [Knut97] for more information on finding Euler Paths in a given graph. The Static Algorithms Class 52 May 15, 1998 The findCycle( method presented above attempts to find a cycle in a given graph by recursively traversing available edges in a depth first search fashion. If at any point, a ....

....degree of zero) however, the isConnected( method does not take this into account, and therefore, a graph that is connected aside from a set of lone vertices will be rejected by the current version of the findEulerPath( method. 47. Refer to 2.3.4.2 of [Knut97] for more details. 48. Refer to [Evan92] and [Knut97] for more information regarding these pre conditions and proof as to why these pre conditions are necessary and sufficient in order for an Euler Path to exist in a given graph. The Static Algorithms Class 53 May 15, 1998 These primary pre conditions are presented in the code as ....

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James R. Evans and Edward Minieka, Optimization Algorithms for Networks and Graphs. Marcel Dekker, Inc., 1992 2nd edition, revised. ISBN 0824786025.

....each node has at most one parent, ranking network structures by sum of weights P n i=1 w(x i ; i ) or by score has the same result. Finding the network structure with the highest weight (l = 1) is a special case of a well known problem of finding maximum branchings described for example in Evans and Minieka (1991). The problem is defined as follows. A tree like network is a connected directed acyclic graph in which no two edges are directed into the same node. The root of a tree like network is a unique node that has no edges directed into it. A branching is a directed forest that consists of disjoint ....

Evans, J. and Minieka, E. (1991). Optimization algorithms for networks and graphs. Marcel Dekker Inc., New York. Learning Bayesian Networks, MSR-TR-94-09 47

....above, we propose an extension of one of the kshortest path algorithms (e. g, Double Sweep Algorithm [Shi 76] which finds the k shortest path lengths between a specified node and all other nodes in the graph) The execution order of this algorithm is about , where represents the number of nodes [Eva 78] 3.1 Extension of the Double sweep algorithm The Double sweep algorithm [Min 78, Shi 74, Shi 76] finds the k shortest path lengths between S i v , min 1 v V tc C m S i , cos tp l j k , S i , cos ( C m SC v , l j k , SL i v , C j C k , C cs i gc C j SC i x ....

R. Evans, Optimization Algorithms for Networks and Graphs, Eds. Marcel Dekker, 1978

....of G 0 if distinct vertices x and y are in N 0 and an arc a between them is in D, then a 2 A 0 . Associate with each arc a of G its time t(a) and cost c(a) The cost of a variant G 0 is the sum of the costs of arcs from G 0 and the time of G 0 is the time of a critical path (see [130]) in G 0 . Given constants C and T , one wishes to find a variant (if any) whose cost is at most C and time is at most T . Any such variant is called acceptable. In h10i we prove the following two results. Theorem 6.21 h10i The problem of deciding if a network has an acceptable variant is ....

E. Minieka, Optimization Algorithms for Networks and Graphs, Marcel Dekker, New York (1978).

....the optimal coloring of the graph is NP complete [8] there are many possible heuristics that we can use. Simple greedy algorithms are preferable because they are fast. Other possibilities include genetic algorithms [9] simulated annealing [10] and algorithms based on optimization problems [11]. The basic tradeoff is between the complexity of the algorithm and the quality of the solution. A simple, fast algorithm is more appropriate in the context of application steering. Realizing a Distribution: Mapping a Coloring to Language Pragmas The colorings produced by an optimization ....

J. R. Evans and E. Minieka, Optimization Algorithms for Networks and Graphs, M. Dekker, New York, 1992.

....in which each node has at most one parent, ranking network structures by sum of weights P n i=1 w(x i ; i ) or by score has the same result. Finding the network structure with the highest weight is a special case of a well known problem of finding maximum branchings described for example in Evans and Minieka (1991). The problem is defined as follows. A tree like network is a connected directed acyclic graph in which no two edges are directed into the same node. The root of a tree like network is a unique node that has no edges directed into it. A branching is a directed forest that consists of disjoint ....

Evans, J. and Minieka, E. (1991). Optimization algorithms for networks and graphs. Marcel Dekker Inc., New York.

....the complexity of an implementation in a digital hardware. It is based on the transformation of costs into time delays. INTRODUCTION The computation of shortest paths in maps of the environment is a basic task of robotics. The two main approaches to this problem are graph searching algorithms [1, 7] and artificial potential fields [5, 6] Artificial potential fields suffer from the existence of undesired local minima, whereas graphsearching algorithms do not have that problem but are often considered time consuming [3] Attempts have been made to accelerate graph searching algorithms using a ....

E. Minieka. Optimization Algorithms for Networks and Graphs. Dekker, New York, Basel, 1978.

.... Gamma (C) 2:1) We omit the proof. Proofs of several special cases of Theorem 2.1 have been published. Dowling [6] gives a proof for Phi = GK ffl n (Example I.6.7) along with a strong converse for that example. Proofs for G = R have appeared in the literature of networks with gains; cf. [12]. A detailed proof for #G = 2 in [15, Section 8A] is based on Equation (2.1) it adapts readily to the general commutative case. There is a dual representation by hyperplanes. In F N let x v be the coordinate corresponding to node v . Let h(e) be the hyperplane specified by x v = e; v; w)xw if ....

E. Minieka, "Optimization Algorithms for Networks and Graphs," Marcel Dekker, New York, 1978. MR 80a:90066.

....of Industrial Engineering and Management, Ben Gurion University of the Negev, BeerSheva 84105, Israel. y Corresponding author. Dept. of Maths and Stats, Brunel University, Uxbridge, Middlesex UB8 3PH, U.K. z Dept. of Math. and Compt. Sci. Odense University, DK 5230, Odense, Denmark. see [8]) in G 0 . Given constants C and T , one wishes to find a variant (if any) whose cost is at most C and time is at most T (the AV problem) Any such variant is called acceptable. We prove that the problem of finding an acceptable variant is NP complete (even if G has a very simple structure) ....

E. Minieka, Optimization Algorithms for Networks and Graphs, Marcel Dekker, New York (1978).

....exhibit exponential time complexities can be solved to near optimality with efficient heuristic techniques. Reasonable solutions can be found to the Travelling Salesperson Problem (TSP) with heuristics such as nearest neighbour search with tour improvement refinement and Christofides heuristic [1]. Another classic NP hard problem is the job shop scheduling problem, of which reasonable solutions can be found with the shifting bottleneck procedure [2] In the realm of GAs, these classic problems have also been studied extensively [3] 4] 5] Efficient genetic operators [6] 7] and ....

J. Evans and E. Minieka, Optimization algorithms for networks and graphs. Second edition, Marcel Dekker, New York, 1992.

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J.R. Evans, E. Minieka, Optimization Algorithms for Networks and Graphs, second ed., Dekker, New York, 1992.

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Evans, J. and Minieka, E. (1992). Optimization Algorithms for Networks and Graphs, 2 nd ed., Marcel Dekker, New York.

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E. Minieka, \Industrial engineering, Optimization Algorithms for Networks and Graphs".

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E. Minieka, "Optimization Algorithms for Networks and Graphs", Marcel Dekker, Inc., 1978.

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J. R. Evans and E. Minieka. Optimization Algorithms for Networks and Graphs, chapter 5, pages 178--233. Marcel Dekker, Inc., second edition, 1992.

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James R.Evans, Edward Minieka, Optimization Algorithms for Networks and Graphs, Marcel Dekker, 1992, Second Edition.

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James R.Evans, Edward Minieka, Optimization Algorithms for Networks and Graphs, Marcel Dekker, 1992, Second Edition.

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Edward Minieka, Optimization Algorithms for Networks and Graphs, Marcel Dekker, Inc., 1978.

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E. Minieka. Optimization Algorithms for Networks and Graphs, Dekker, 1978.

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