Speeding up the estimation of expected maximum flows through reliable networks

  • 25 Pages
  • 0.60 MB
  • English
Indian Institute of Management , Ahmedabad
StatementMegha Sharma, Diptesh Ghosh.
SeriesWorking paper -- W.P. no. 2009-04-05
ContributionsIndian Institute of Management, Ahmedabad.
LC ClassificationsMicrofiche 2009/60114 (Q)
The Physical Object
Pagination25 leaves
ID Numbers
Open LibraryOL23955401M
LC Control Number2009346856

Speeding Up the Estimation of Expected Maximum Flows Through Reliable Networks Megha Sharma Diptesh Ghosh W.P. April The main objective of the Working Paper series of IIMA is to help faculty members, research staff, and doctoral students to.

Speeding Up the Estimation of Expected Maximum Flows Through Reliable Networks. Downloadable. In this paper we present a strategy for speeding up the estimation of expected maximum flows through reliable networks.

Our strategy tries to minimize the repetition of computational effort while evaluating network states sampled using the crude Monte Carlo method. Computational experiments with this strategy on three types of randomly generated networks show.

Description Speeding up the estimation of expected maximum flows through reliable networks FB2

Computing the probability mass function of the maximum flow through a reliable network Megha Sharma1 Diptesh Ghosh2 Abstract In this paper we propose a fast state-space enumeration based algorithm called TOP-DOWN to compute the probability mass function of the maximum s-t flow through reliable networks.

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood.

Classical algorithms for computing maximum s-t flows are among the most well-known combinatorial graph algorithms [28, 30,35,40], and there is an extensive body of research dedicated to finding. Runoff may be classified according to speed of appearance after rainfall or melting snow as direct runoff or base runoff, and according to source as surface runoff, storm interflow, or groundwater runoff.

The sum of total discharges described in (1), above, during a specified period of time. Maximum flows algorithms The Ford Fulkerson method can be used to solve the maximum flow problem.

The method is iterative. It proceeds by finding an augmenting path in the residual network through which we can ship more flow. This is ended when no augmenting path can be found and we have achieved maximum flow. L'Ecuyer P, Saggadi S and Tuffin B Graph reductions to speed up importance sampling-based static reliability estimation Proceedings of the Winter Simulation Conference, () Herrmann J and Soh S Comparison of binary and multi-variate hybrid decision diagram algorithms for k-terminal reliability Proceedings of the Thirty-Fourth Australasian.

optical flow networks and optimized the hyperparameters by experimentation. After training on cloud GPUs for several epochs, we were able to achieve 91% accuracy on unseen test frames.

INTRODUCTION The ultimate goal of this research was to estimate the speed of a car in real time. Min-Cost Max-Flow A variant of the max-flow problem Each edge e has capacity c(e) and cost cost(e) You have to pay cost(e) amount of money per unit flow flowing through e Problem: find the maximum flow that has the minimum total cost A lot harder than the regular max-flow – But there is an easy algorithm that works for small graphs Min-cost Max-flow Algorithm State A represents normal approaching traffic flow, again at speed v f.

State U, with flowrate q u, corresponds to the queuing upstream of the truck. On the fundamental diagram, vehicle speed v u is slower than v f. But once drivers have navigated around the truck, they can again speed up and transition to downstream state D.

In optimization theory, maximum flow problems involve finding a feasible flow through a flow network that obtains the maximum possible flow rate. The maximum flow problem can be seen as a special case of more complex network flow problems, such as the circulation maximum value of an s-t flow (i.e., flow from source s to sink t) is equal to the minimum capacity of an s-t cut (i.e.

On one hand, a large proportion of authors (group A) have handled the estimation through an adapted power flow. Authors in [8] propose a probabilistic load flow where calculate expected values and standard deviations associated to them using a huge database which includes daily load curves (DLCs).

Maximum flow problems involve finding a feasible flow through a single-source, single-sink flow network that is maximum. Let’s take an image to explain how the above definition wants to say. Each edge is labeled with capacity, the maximum amount of stuff that it can carry.

A linearized active power flow model is built up and loop analysis method with introduction of series potential compensation is used to compute the distribution of the flows after occurance of outages, thereby the change of active power performance index due to outage is calculated, and from which an outage list is constructed.

much greater than the depth of flow is a good approximation to a flow with infinite width. 8 Take the x direction to be downstream and the y direction to be normal to the boundary, with y = 0 at the bottom of the flow (Figure ). By the no-slip condition, the velocity is zero at y = 0, so the velocity must increase upward in the flow.

Flow networks and flows. A flow not absolute value or cardinality.) In the maximum-flow problem, we are given a flow network G with Treating the residual network G f in the figure as a flow network, we can ship up to 4 units of additional net flow through each edge of this path without violating a capacity constraint, since the.

Free-flow speed (FFS) is the drivers’ desired speed on roadways at low traffic volume and absence of traffic control devices whose determination is a fundamental step in the analysis of two-lane.

single origin node and a single destination node, the maximum possible flow from origin to destination equals the minimum cut value for all cuts in the network.

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This may seem surprising at first, but makes sense when you consider that the maximum flow through a series of linked pipes equals the maximum flow in the smallest pipe in the series, i.e. Consider a flow network (𝑉,𝐸,𝑐), and let, ′∈𝐸be anti-parallel edges. Prove that there exists a maximum flow in which at least one of, ′has no flow through it.

a b Solution Consider a maximum flow. If either or ′has no flow through it in, we are done. It is defined as the maximum amount of flow that the network would allow to flow from source to sink.

Multiple algorithms exist in solving the maximum flow problem. Two major algorithms to solve these kind of problems are Ford-Fulkerson algorithm and Dinic's Algorithm.

Definitive estimates: Drafted when a project’s scope and constituent tasks are almost fully defined, a definitive estimate makes full use of deterministic estimating techniques, such as bottom-up estimating. Definitive estimates are the most accurate and reliable and are used to create bids, tenders, and cost baselines.

Dinic EA () Algorithm for solution of a problem of maximum flow in networks with power estimation. Soviet Math Dokl 11 J ACM – CrossRef zbMATH Google Scholar.

Details Speeding up the estimation of expected maximum flows through reliable networks PDF

Elias P, Feinstein A, Shannon CE () Note on maximum flow through a network. IRE Trans Inform Theory IT Search book. Search within book. Type for. We develop such a scheme, and achieve up to two orders of magnitude speed-up in estimation time over the previously proposed two-runs-based RATE scheme [Kodialam, M et al., ].

The speedups are achieved without a significant increase in. Speeding up Maximum Flow Computations on Shared Memory Platforms Bachelor Thesis of Niklas Baumstark At the Department of Informatics Institute for Theoretical Computer Science Reviewer: Prof.

Peter Sanders Second reviewer: Prof. Dorothea Wagner Advisor: Prof. Peter Sanders Second advisor: Prof. Guy Blelloch. 6 MAXIMUM FLOWS IN NETWORKS and a vertex cover Cwith jMj= jCj. For this purpose, we construct a ow network with vertex set V = fs;tg[X[Y and arc set A= f(s;x): x2Xg[E[f(y;t): y2Yg; where Eis the edge set of the given bipartite graph.

Let the capacities of all arcs be equal to 1, and consider a maximum ow ˚(that is, ˚(a) denotes the ow on. for all the other arcs, the minimum cost flow problem will send the maximum feasible flow through the other arcs, which achieves the objective of the maximum flow problem.

Applying this formulation to the Seervada Park maximum flow problem shown in Fig. yields the network given in Fig.where the numbers given next to the original. maximum number of barrels that can be pumped through the pipeline per hour.

The company wants to s 1 2 t 3 2 3 2 4 1 3 Figure 1. A pipeline network know the maximum number of barrels they can pump per hour from node s to node t. This is an instance of the maximum ow problem, a fundamental optimisation problem that comes up over and over again in.

0 = speed at which no crush is expected; BP 1 = slope of speed versus crush (change in impact speed to change in crush.); CRM = Maximum Crush (in). A published table provided values for BP 0 and BP 1.

The variables are based on the weight of the vehicle. MORGAN AND IVEY This equation, named for its authors, was presented in an SAE paper in. Maximum flows in probabilistic networks Maximum flows in probabilistic networks Nagamochi, Hiroshi; Ibaraki, Toshihide 1. INTRODUCTION The reliability of capacitated networks subject to random arc failures.

such as communication, transportation, power, and water networks. is often evaluated by such measures as probabilistic connectedness and expected value of maximum flow.A Note on the Maximum Flow Through a Network* P. ELIASt, A. FEINSTEINI, AND C. E. SHANNON! Summary--This note discusses the problem of maximizing the rate of flow from one terminal to another, through a network which consists of a number of branches, each of which has a!imited capa- city.If we continue selecting this pair of flow-augmenting paths, we will need a total of 2 U iterations to reach the maximum flow of value 2 U (Figure d).

Of course, we can obtain the maximum flow in just two iterations by augmenting the initial zero flow along the path 1 → 2 → 4 followed by augmenting the new flow along the path 1 → 3 → 4.