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If we substitute this in (3.2.1) and (3.2.2) there is cancellation: + S C -s -s s s = mdt + s - s sdz + s - s m dt + s - s s dz s - s -s s = m dt. m+ s - s s - s The portfolio has no risk, only an expected return, and the expected return of a portfolio with no risk must be the risk-free rate: -s s s - s m + s - s m = rf or m - rf = s (m - rf ). s (3.2.5) (3.2.4)

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Here we follow the development by Fano and Lawson (1948) but use the S and T parameters de ned in Section 2.2. Common passive networks are reciprocal so that, if a driving voltage or current at one port produces a response current or voltage, respectively, at a second port, then reversing the network will cause the same drives to produce the same responses in the opposite direction. We will use the normalized voltages of Eq. (2.16), which, by that equation, can be expressed as the square root of the product of voltage and current. Reciprocity then applies to both the currents and voltages and, as a result, to their products, giving S12 = S21 . (30) Since the networks are lossless, the power must either be transmitted or re ected, leading to |S11 |2 + |S21 |2 = 1 (31) and |S22 |2 + |S12 |2 = 1. Combining the last three equations, we see that |S11 | = |S22 |. (33) (32)

Being objective and honest about where you stand relative to best in class is an important piece of the assessment. It helps you prioritize your restructuring efforts and build the balanced scorecard measurements that are necessary to track throughout the project and beyond. How did you do Are there areas that stand out as needing immediate attention Now it s time to look at how to tailor your restructuring effort to address immediate needs, along with a long-term holistic approach to restructure and revamp your entire business. This initiative will be critical to the speed and extent of success of your restructuring, and will be how the business operates for a few years, so take time to do it right.

Again, j > 0, and q j . j / is interpreted as a rate of ow in link j corresponding to a rate of charge in that link of j . This rate of charge adjusts itself proportionally to the excess demand (the term in the right-hand side of (10.11)). This is similar to the tatonnement process of Section 5.4.1. As with the primal algorithm, it can be shown that V. / is strictly concave in and that it is a Lyapunov function for the system (10.11) (10.12). Thus, converges to the maximizer of V. 10.2.3 User Adaptation So far, we have assumed that the wr s are xed on the timescales of the operation of the primal and dual algorithms. In this case NETWORK is solved by assigning each user r a rate xr . Now suppose that the wr s are allowed to change. A simple iteration between users and network could take place as follows. The users could wait until the xr s in NETWORK converge. Then, after computing the resulting prices based on their previous selections of wr s and the newly de ned xr s, they could solve their local USERr problems and make new choices of wr s. These could become new inputs to NETWORK, which could be resolved, and so on. A more interesting iteration is when both problems are solved at the same time. Observe that xr .t/ is de ned by the differential equation (10.9), which user r can affect only through his choice of wr .t/. Suppose that he monitors xr .t/ continuously and varies wr .t/ so that

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