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(25.85) The unbiased sample estimate of cov(du , dv ) is sdu ,dbv = (n 1)

1, 2, 3, 4, 6

let u be such that L(u) = j; greedily color node u; The function Labeler, which assigns a label between 1 and n to each node, is defined as follows: Labeler(G, ) if G is not empty let u be a vertex of G of minimum degree; L(u) ; Labeler(G u, 1); Here, G-u is the graph obtained from G by removing u and all incident edges. It is straightforward to see that progressive_min_deg_last produces a legal coloring, since the coloring is performed in a greedy fashion. This will be true for all of the algorithms described in this chapter, and we will make no further reference to algorithm correctness. Theorem 4 For a planar graph, progressive_min_deg_last is 9-approximate.

A custom Function procedure has a lot in common with a Sub procedure. (For more information on Sub procedures, see 9.)

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re you bored with the look of Windows XP Are you tired of staring at the same, dull icons and desktop wallpaper or hearing the same, bland sound effects Then give XP a complete overhaul by customizing its audio and visual features courtesy of the 5-minute xes in this chapter.

PURPOSE TYPE NESTED WITHIN START TAG END TAG CONTENT ATTRIBUTES EXAMPLE TIP Specifies an absolute URL to use as the basis for resolving relative URLs in your document inline

this Markov chain from the uniform distribution, which already is the maximum entropy distribution, the distribution will tend to the stationary distribution, which has a lower entropy than the uniform. Here, the entropy decreases with time. If, however, the stationary distribution is the uniform distribution, we can express the relative entropy as D( n || ) = log |X| H ( n ) = log |X| H (Xn ). (4.47)

12: Custom Dialog Box Alternatives . 13: Introducing UserForms . . . . . . 14: UserForm Examples . . . . . . . . 15: Advanced UserForm Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 377 411 439

where b is the log-optimal portfolio for the conditional distribution of Xi i given the past values of the stock market; that is, b (x1 , x2 , . . . , xi 1 ) is i the portfolio that achieves the conditional maximum, which is denoted by max E[ log bt Xi |(X1 , X2 , . . . , Xi 1 ) = (x1 , x2 , . . . , xi 1 )]

Select the Text tool. If the Property inspector is not already displayed, choose Window > Property inspector. In the Property inspector, click the triangle next to the Font text box and select a font from the list, or enter a font name.

5.2 LOWER BOUNDS In order to evaluate any algorithm and to be able to give bounds on its performance ratio, it is essential to have good lower bounds. Some lower bounds, such as those based on the maximum demand in a cell, are straightforward to obtain. Others can be derived from representations of the channel assignment problem as a graph coloring problem or a traveling salesman problem. In this section, I will give an overview of the lower bounds available for channel assignment with constraints. An early paper by Gamst [12] presents a number of lower bounds based on sets of nodes that have a prescribed minimum constraint between them. More precisely, a dclique in a constrained graph G = (V, E, c0, c1, . . .) is a set of nodes so that for any pair of nodes u, v, dG(u, v) d. Note that a d-clique corresponds to a clique in Gd, the graph obtained from G by adding edges between all pairs of nodes with distance at most d in G. Any two nodes in a d-clique have constraint at least cd between them, and thus any two channels assigned to nodes in the d-clique have to have separation at least cd. This leads

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