e InB from the same channel), and notic- ing that crosstalk happens only in the traversed OXCs whose number can be calculated from the elements of the matrix [1R], a new equation for the noise is obtained (10.26) where Pm is the maximum power allowed by the transmitter, always in the hypothesis, to neglect nonlinear effects. Once the structure of the receiver is known, for example, if there is an error detecting code (EDC), and the value of the DCF dispersion (that has to be known a priori), the resid- ual dispersion penalty can be generally neglected. Calling the margin (in linear units), Equation 10.22, rewritten in linear units, yields to this expression (10.27) Equation 10.27 is a linear constraint that can be easily inserted in an ILP optimization algorithm. The situation changes completely if nonlinear propagation is important in the network. In this case, the nonlinear induced penalty depends on the span length and on the trans- mitted power. This means that the equivalent of Equation 10.25 becomes a nonlinear relationship between the coefficients of the matrix [1R], and the ILP cannot be used to solve the result- ing problem. This consideration has to be combined with the fact that the ILP design is quite more cumbersome if carried out for a transparent network with respect to an opaque one, due to the need of representing the inner structure of the OXC in order to deal with the use of WCs, the need of managing the physical wavelength continuity, and the need of adding Telecommunication Networks several constraints (one for each wavelength path) in order to take into account transmis- sion impairments. In Chapter 8, we have optimized an opaque network with 15 nodes and more than 30 links on a good workstation simply accepting an optimality gap of about 3%. If we con- sider a T-OTN on the same topology taking into account linear transmission impairments, our algorithm does not converge in 24h of work. This is the reason [43,44] why several heuristic algorithms are born to face this prob- lem with optimizat