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The Optimal Allocation problem is to determine what fraction (xi ) of the b code blocks should be sent on the ith path, subject to the path volume constraint, to maximize the overall probability of message delivery Formally, let Y = n xi Si Find (x 1 , x2 , , xn ) that maximize Prob(Y i=1 r y1), where n xi = 1 and i 1 n, 0 xi Vi /mr i=1 By xing the replication factor and treating delay as a constraint, they formulate the problem as an optimal allocation problem and consider two cases of path failure scenarios: Bernoulli (0 1) path failure and partial path failures In the Bernoulli path failure case, when a path fails, all the messages sent over the path are lost In the partial path failure case, some messages can be recovered with certain probabilities.

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class LongDouble { public: LongDouble( double dval ) : value( dval ) { } bool compareLess( const LongDouble & ); private: double value; };

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However, no operator<() exists to compare two values of type LongDouble, and the member functions min() and max() cannot be used with a Queue of type Queue<LongDouble> One solution to this problem is to define global operators operator<() and operator>() that use the LongDouble member function compareLess() to compare two values of type Queue< LongDouble> These global operators are then invoked automatically within min() and max() to compare the items in a Queue of type Queue<LongDouble> However, to introduce class template specializations, we consider another solution We do not want the generic member function definitions for the class template Queue to be used to instantiate the member functions min() and max() if the template argument is the class type LongDouble Instead, we want to define instances for Queue<LongDouble>::min() and Queue<LongDouble>::max() that use the LongDouble member function compareLess() We can do this by providing a specialized definition for a member of a class template instantiation using an explicit specialization definition An explicit specialization definition is a definition in which the keyword template is followed by the less than (<) and the greater than (>) tokens, followed by the definition of the specialization for the class member In the following example, explicit specializations are defined for the member functions min() and max() of the class template instantiation Queue<LongDouble>:

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// explicit specialization definitions template<> LongDouble Queue<LongDouble>::min( ) { assert( ! is_empty() ); LongDouble min_val = front->item; for ( QueueItem *pq = front->next; pq != 0; pq = pq->next ) if ( pq->itemcompareLess( min_val ) ) min_val = pq->item; return min_val; } template<> LongDouble Queue<LongDouble>::max( ) { assert( ! is_empty() ); LongDouble max_val = front->item; for ( QueueItem *pq = front->next; pq != 0; pq = pq->next ) if ( max_valcompareLess( pq->item ) ) max_val = pq->item; return max_val; }

Even though the class type Queue<LongDouble> is instantiated from the generic class template definition, each object of type Queue<LongDouble> uses the specializations for the member functions min() and max() these member functions are not instantiated from the generic member function definitions for the class template Queue

d2 is the maximum distance between large exactly-represented numbers; xmin is the maximum distance between 0 and the smallest exactly-represented numbers: where the adjectives small and large refer to the absolute value of the corresponding numbers. Every number x will be represented in the form +s.be, with b ! 2, s being the signi cand and e the exponent. In order to make the implementation of the arithmetic operations easier (Section 16.2), the two following conditions must be satis ed: 1. The signi cand s is represented in base B b. 2. The signi cand belongs to the interval 1 Thus x is expressed in the form (1:s 1 s 2 ::s p ):B, Bemin B :B B p :Bemax

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (780 / 1065) [2001-3-29 11:32:12]

They proved that the optimal allocation problem under the Bernoulli path failure case, which is formulated as a mixed integer programming, is NP hard For the partial path failure case, they rst show that maximizing the successful probability is equivalent to maximizing the Sharpe ratio, which plays an important role in the theory of allocation assets in investment portfolios (see references in reference 38 for more details) They propose to use approximation approaches from economic theory to maximize the ratio The solution of the optimal allocation problem is static and does not change over time because it is assumed that the underlying path failure probability does not change over time.

Because the explicit specialization definitions for the member functions min() and max() are function definitions and not template definitions, (and because these definitions are not declared inline), they cannot be placed in a header file They must be placed in a program text file Fortunately, it is possible just to declare a function template explicit specialization without defining it For example, the explicit specialization for the member functions min () and max() can be declared as follows

// declarations of function template explicit specialization template<> LongDouble Queue<LongDouble>::min( ); template<> LongDouble Queue<LongDouble>::max( );

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