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The weighted clique number, denoted (G, w) is the maximum weight of any maximal clique in the graph The weighted chromatic number and clique number of a call graph are defined in an analogous fashion For integers k 1, we define the k-locality of a node v to be the induced subgraph consisting of those nodes in G whose graph distance from v is less than or equal to k Given G = (V, E), the graph Gr = (V, E ) is defined by E = E E2 Er 1 Thus any pair of nodes at distance i < r in G is connected by an edge in Gr The problem of channel assignment in a weighted graph (G,w) with reuse distance r is thus the same as multicoloring the graph Gr.



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ef ciency, employees nd themselves thinking in ef cient terms Quality itself is usually the result of ef cient thinking When management recognizes this relationship, they can lead corporate thinking into more ef cient modes People begin to see various ways in which they can achieve better quality through improvements in their own process ef ciency This is where the ef ciency expert fails By emphasizing the cost of process rather than critically evaluating how processes can be changed and improved, the expert misses the point One revelation that most people come upon when thinking in quality mode is that the single most important aspect of ef ciency is the level of work in process In a manufacturing environment, this means goods in the process of being produced from raw materials through to nished goods.





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The unweighted clique number of Gr is the maximum size of any clique in Gr, and is denoted (Gr) Similarly, the chromatic number of Gr, the minimum number of colors needed to color Gr, is denoted by (Gr) It is known that when G is a hexagon graph, (Gr) = (Gr) (see Section 453), and an optimal coloring can be computed in polynomial time We assume that such an optimal coloring of the graph Gr is available; thus, every node in Gr is assigned a color from the set {1, 2, , (Gr)} If G is a hexagon graph, we say that G2 is a square graph Figure 41 shows a base coloring with red, blue, and green (r, b, and g) colors for a hexagon graph It is easy to see that a square graph can be base colored with seven colors; Figure 4.

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As you learned in Understanding Layouts and Themes at the beginning of this chapter, themes are the PowerPoint 2010 way of applying different designs to the presentation. A theme usually includes a background graphic, color and font choices, and graphic effect settings. A theme can also include custom layouts. The method for applying a theme depends on whether that theme is already available in the current presentation or not. Some themes are built into PowerPoint so that they are always available; other themes are available only when you use certain templates or when you speci cally apply them from an external le. The following sections explain each of those possibilities.

Of course, the more volume of work in process, the less ef ciently work can be performed This manufacturing concept applies to all processes For example, a Shipping and Receiving Department is going to be less ef cient when its work load is excessive, because work in process takes up time, and the more time spent not delivering the nal result of the process, the less quality there is in the entire matter, from beginning to end So the question becomes: How do I reduce work in process to improve ef ciency So rather than trying to gure out how to speed up a awed process, we get to the point of controlling work ow itself The result greater ef ciency and, of course, higher quality translates to lowered processing costs, improved customer satisfaction, and improved employee morale.

8 shows such a coloring We define Nr(v) to be all neighbors of v in Gr, and Hr(v) to be w(v) + u Nr(v)w(u) Finally, given a assignment of channels to Nr(v), RCr(v) is defined to be the number of channels assigned to more than one node in Nr(v) Thus RCr(v) is a measure of the reuse of channels within Nr(v) 422 Performance Measures An approximation algorithm for the static channel assignment problem is said to have performance ratio r if on any weighted graph (G, w), the algorithm uses at most r (G, w) + O(1) channels We use a standard yardstick for measuring the efficacy of online algorithms: that of competitive ratios [25, 3] Given an online algorithm P that processes a sequence of N call graphs (G, Ct), t = 0, , N, let S(Pt) denote the span of the channel as-.

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