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In the event of a failure of the link R1 R2, the route from S to D is through node R3 If S starts forwarding traffic to R3, packets will not arrive at R1 and at the protection path This can be a problem if the IGP on R3 has not yet performed its SPF and still points to S as its best path (the route from before the failure) In this case, traffic will loop between S and R3 This situation is referred to as microloops and there are several proposals for avoiding it The most intuitive is to slow down the convergence of the routers so as to avoid the situation where node S reconverged faster than node R3 The solution to the microloop problem is currently being worked on by the Routing Area Working Group in the IETF.

Apart from the issue of microloop prevention, a tunnel-based approach is attractive for the following reasons: Most elements of the solution have been deployed in networks today in the context of MPLS FRR The computations are well bounded (one computation per link for link protection, or one per next next-hop for node protection)..

/*********************************************************************************/ calcMCGAsianPrice : computes the price of an geometric Asian option with a control variate using Monte Carlo simulation [in]: double price : initial stock price double strike : strike price double vol : stock volatility double rate : risk-free rate double div : dividend yield double T : time to maturity char type : (C)all or (P)ut int M : number of simulations int N : number of time steps [out]: double : price of geometic Asian option with a control variate **********************************************************************************/ double AsianOption::calcMCAAsianGCV(double price, double strike, double vol, double rate, double div, double T, char type, int M, int N) { // initialize variables int i, j; double geo = 0.0; // geometric average double ave = 0.0; // arithmetic average

E g(F1 (t1 ), . . . , FN (tn )

9.3 9.3.1

N equal time steps t = (T t)/N and simulate a path {S(ti), i = 0, 1, . . . , N} starting at the known state (initial price) S0 at time t0. Since we already found an exact solution to the stochastic differential equation (SDE) in equation (1.8), we know that over a time step t the stock price changes according to: 2 Si +1 = Si exp m t + t i +1 2 = Si +1 = Si exp t + t i +1

The same configuration will continue for a long period (minutes/hours), until a /2 hop of the recovered carrier, due to noise, leads to another configuration. One simple way to overcome this difficulty is to use a differential encoder/decoder scheme. In this case, a single encoded symbol does not carry the phase of the corresponding transmitted symbol, but the difference between the phases of two consecutive encoded symbols is equal to the phase of the transmitted symbol. The differential decoder only has to implement a phase subtraction to recover the information. Thus, the ambiguity is removed but with the following penalty: one transmission error now corresponds to two consecutive errors in the decided phase variation, which results (with the convenient modulation algorithm, e.g. Gray coding) in two bit errors and therefore a greater BER for a given C/N. It is interesting to note that a burst of N errors due to a /2-hop does not result in 2N errors but only in two errors, one at the beginning of the burst and one at the end. In other words, two consecutive errors do not give rise to an error if they are "coherent", i.e. phase-shifted by the same value. However, such a case of two coherent consecutive errors has a low probability of occurrence. The global error probability is thus approximately doubled by a differential encoding/decoding scheme.

Removing the duplicates can be as easy as manually deleting records from your Find Duplicates query.

Most relational database products available today use a standardized language for manipulating the data within a database. This language is known as the Structured Query Language, or SQL (often pronounced as sequel ). Although the SQL language is standardized, most implementations conform only to a subset of the complete standard language known as SQL-2 Entry Level. The subset serves as a least common denominator between most database products. The databases then create their own extension beyond this standardized subset. In practice, this means that each dialect of SQL is slightly different, although the basic syntax remains the same.