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BinarySearch() uses a binary search algorithm and requires that the elements be sorted A useful feature of the BinarySearch() method is that if the element is not found, a negative integer is returned The bitwise complement (~) of this value is the index of the next element larger than the element being sought, or the

isValid()

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19.3.5 Multihop and feasible lower bounds under high attenuation The O(n) upper bound on transport capacity is tight for regular planar networks in media with > 0 or > 3, and it is achieved by multihop. The multihop strategy is de ned as the following. Let denote the set of all paths from source s to destination d , where by such a path we mean a sequence (s = j0 , j1 , . . . , . . . , jz = d ) with jq = jr for q = r . The total traf c rate R to be provided to the source destination pair (s , d ) is split over the paths in in such a way that if traf c rate 0 is to be carried over path , then = R . On each path , packets are relayed from node to next node. On each P such hop, each packet is fully decoded, treating all interference as noise. Thus, only pointto-point coding is used, and no network coding or multiuser estimation is employed. Such a strategy is of great interest and it is currently the object of much protocol development activity. The following result implies that, when > 0 or > 3, the sharp order of the transport capacity for a regular planar network is (n), and that it can be attained by multihop. (r5) In a regular planar network with either > 0 or > 1, and individual power constraint Pind CT S e 2 Pind c3 ( , )Pind + 2 n

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total element count if there is no greater value This provides a convenient means to insert new values into the list at the specific location so as to maintain sorting (see Listing 122)

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2 4e 4 4(1 + 4 )e , 2 ) 2 (1 e c3 ( , ) := 16 2 + (2 16) , ( 1)(2 1)

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using System; using SystemCollectionsGeneric; class Program { static void Main() { List<string> list = new List<string>(); int search; listAdd("public"); listAdd("protected"); listAdd("private"); listSort(); search = listBinarySearch("protected internal"); if (search < 0) { listInsert(~search, "protected internal"); } foreach (string accessModifier in list) { ConsoleWriteLine(accessModifier); } } }

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and S(x) denotes the Shannon function S(x) := log(1 + x) /2 This order of distance weighted sum of rates is achievable by multihop Multihop is order-optimal in a random scenario over a regular planar network in media with > 0 or > 3, providing some theoretical justi cation for its use in situations where traf c is diffused over the network Consider a regular planar network with either > 0 or > 1, and individual power constraint Pind The n source destination pairs are randomly chosen as follows: every source s is chosen as the node nearest to a randomly (uniformly iid) chosen point in the domain, and similarly for every destination d Then lim Prob [ R = c/ (n log n) is feasible for n every {1, 2, , n}] = 1 for some c > 0 Consequently, a distance weighted sum of .

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Beware that if the list is not first sorted, an element will not necessarily be found, even if it is in the list The results of Listing 122 appear in Output 122

- Sets whether the contents of this prefer-

rates CT = [n/ (log n)] is supported with probability approaching one as This is within a factor l/ log n of the transport capacity (n) possible when > 3 (r6) A vector of rates (R1 , R2 , , Rm ) can be supported by multihop in a planar network in media with > 0 or > 1, if the traf c can be load balanced such that no node is overloaded and no hop is too long This is a fairly straightforward result saying nothing about order optimality, and is provided only in support of the above theme that multihop is an appropriate architecture for balanceable scenarios (r7) A set of rates (R1 , R2 , .

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