Modeling and Evaluation with Graph Mohammad Khalily Dermany Islamic Azad University, Khomein branch.
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Transcript of Modeling and Evaluation with Graph Mohammad Khalily Dermany Islamic Azad University, Khomein branch.
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Modeling and Evaluation with GraphMohammad Khalily Dermany
Islamic Azad University, Khomein branch.
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2Application
Graphs useful mathematical objects for representing many physical systems.
Communication
systematical analyze of electrical circuits
Relationship of data
Distributed Computing on a multi-Processor Computers
Cloud computing
Other science fields
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3basic questions
Shortest path problem
Maximum flow problem
Minimum cost flow problem
Assignment problem
Transportation problem
Circulation problem
Convex cost flow problems
Generalized flow problems
Multicommodity flow problems
Minimum spanning tree problem
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4Notation
Let G = (N, A) be a directed network defined by a set N of n nodes and a set A of m directed arcs. Each arc (i, j)EA has an associated cost Cij that denotes the cost per unit flow on that arc. We assume that the flow cost varies linearly with the amount of flow. We also associate with each arc (i, j)EA a capacity Uij that denotes the maximum amount that can flow on the arc and a lower bound lij that denotes the minimum amount that must flow on the arc. We associate with each node iEN an integer number b(i) representing its supply/demand. If b(i) > 0, node i is a supply node; if b(i) < 0, node i is a demand node with a demand of - b(i); and if b(i) = 0, node i is a transshipment node.
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5Definition
Tails and Heads
Degrees
Adjacency List
Multiarcs and Loops
Subgraph
Walk
Directed Walk
Path: A path is a walk without any repetition of nodes.
Directed Path
Cycle
Acyclic Graph
Connectivity
Strong Connectivity
Cut
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6Various Types of Shortest Path Problems
1. Finding shortest paths from one node to all other nodes when arc lengths are nonnegative
2. Finding shortest paths from one node to all other nodes for networks with arbitrary arc lengths
3. Finding shortest paths from every node to every other node
4. Various generalizations of the shortest path problem
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7SHORTEST PATHS
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8iterative algorithmic approaches
label setting (Dijkstra' s algorithm O(n2))
several versions of Dijkstra's algorithm that improve upon its worst-case complexity.
implementations uses a heap (or priority queue) data structure
Knapsack Problem
label correcting (O(n2C) C on the maximum absolute value of any arc length)
reduces the distance label of one node at each iteration by considering only local information, namely the length of the single arc and the current distance labels of its incident nodes.
flexibility
Label-setting algorithms designate one label as permanent (optimal) at each iteration. In contrast, label-correcting algorithms consider all labels as temporary until the final step, when they all become permanent.
modified versions
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9
Dijkstra' s algorithm
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10Label-Correcting Algorithm
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11Label-Correcting Algorithm (continue)
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12Label-Correcting Algorithm (continue)
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13Label-Correcting Algorithm (continue)
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14Label-Correcting Algorithm (continue)
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15Label-Correcting Algorithm (continue)
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16Label-Correcting Algorithm (continue)
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17OPTIMALITY CONDITIONS of Label-Correcting Algorithm
they must satisfy the following necessary optimality conditions
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18ALL-PAIRS SHORTEST PATH PROBLEM
repeated shortest path algorithm
If S(n, m, C) denotes the time needed to solve a shortest path problem
All-pairs shortest path problem in O(n S(n, m, C))time.
all-pairs label-correcting algorithm
especially well suited for dense networks
Floyd-Warshall algorithm
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19MAXIMUM FLOWS
The maximum flow problem and the shortest path problem are complementary
Shortest path problems model arc costs but not arc capacities; maximum flow problems model capacities but not costs.
we wish to send as much flow as possible between two special nodes, a source node s and a sink node t, without exceeding the capacity of any arc.
The max-flow min-cut theorem establishes an important correspondence between flows and cuts in networks.
Indeed, as we will see, by solving a maximum flow problem, we also solve a complementary minimum cut problem: From among all cuts in the network that separate the source and sink nodes, find the cut with the minimum capacity.
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20MAXIMUM FLOWS (continue)
s-t cut: a cut is a partition of the node set N into two subsets Sand S = N - S;
Capacity of an s-t cut: sum of the capacities of the forward arcs in the cut
Minimum cut: an s-t cut whose capacity is minimum among all s-t cuts as a minimum cut.
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21MAXIMUM FLOWS (continue)
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22MAXIMUM FLOWS (continue)
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23Finding Minimum Cuts Using Shortest Paths
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24MINIMUM COST FLOWS APPLICATIONS
Distribution Problems
Min energy routing
Resource allocation
TCP protocol
MAC Layer Fair Rate Control
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25MINIMUM COST FLOWS
shortest path problem contains no arc capacities
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26MINIMUM COST FLOWS
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27potential of node
(i) as the potential of node I
(i) is the linear programming dual variable corresponding to the mass balance constraint of node i.
reduced cost of an arc (i, j) as = cij - (i) + (j).
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28potential of node
the node potentials do not change the shortest path between any pair of nodes k and I, since the potentials increase the length of every path by a constant amount (l) - (k).
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29
SUCCESSIVE SHORTEST PATH ALGORITHM PRIMAL-DUAL ALGORITHM
It maintains a solution x that satisfies the nonnegativity and capacity constraints, but violates the mass balance constraints of the nodes.
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30SUCCESSIVE SHORTEST PATH ALGORITHM PRIMAL-DUAL ALGORITHM(continue)
A pseudofllow is a function x: A -> R+ satisfying only the capacity and nonnegativity constraints; it need not satisfy the mass balance constraints. For any pseudoflow x, we define the imbalance of node i as
If e(i) > 0 for some node i, we refer to e(i) as the excess of node i; if e(i) <0, we call - e(i) the node's deficit. We refer to a node i with e(i) = 0 as balanced.
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31SUCCESSIVE SHORTEST PATH ALGORITHM PRIMAL-DUAL ALGORITHM(continue)
Let E and D denote the sets of excess and deficit nodes in the network.
if the network contains an excess node, it must also contain a deficit node.
classical in the sense that researchers developed them in the 1950s and 1960s
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32RELAXATION ALGORITHM
a more recent vintage minimum cost flow algorithm
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33RELAXATION ALGORITHM(continue)
each flow variable Xij appears twice: once with a coefficient of - (i) and the second time with a coefficient of (j).
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34RELAXATION ALGORITHM(continue)
(1) if > 0, we set Xij = 0;
(2) if < 0, we set Xij = uij;
(3) if = 0, we can set xij to any value between 0 and Uij.
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