"... In PAGE 26: ...Table 11: Impact of non-convexity These cases are analyzed in Table11 , where, in percentages, \robust nominal quot; is the nominal return attained by the optimal solution to the robust optimization problem and \robust worst case quot; is the worst-case return it attains under the uncertainty model; \robust positions quot; is the number of positions taken by the robust portfolio. From an aggregate perspective all six cases are equivalent: the adversary can, in each case, decrease returns by a total \mass quot; of 100.... In PAGE 26: ... From an aggregate perspective all six cases are equivalent: the adversary can, in each case, decrease returns by a total \mass quot; of 100. Yet, as we can see from Table11 , the six cases are structurally quite di erent. It appears, therefore, that a smooth convex model used to replace our histogram structure would likely produce very di erent results in at least some of the six cases.... ..."

"... In PAGE 5: ... The destination node receives multiple request messages but chooses the best route with respect to the given power metric. Table2 summarizes the GEAR algorithm. Table 2.... ..."

"... In PAGE 2: ...igure 6: Measured performance of three different mm-wave signal generation PICs. .... 8 Tables Table1... In PAGE 11: ... Harmonic mixers were used at the input of the spectrum analyzer for heterodyne detection of frequencies above 50 GHz. Table1 details the required bias conditions of the three ring diameters for stable mode- locking. Table 1: Operating currents and voltages for selected mm-wave generation PICs.... In PAGE 12: ... Labeled ring dimensions are the diameter of ring laser used in the PIC to obtain the corresponding signal frequency. Gain of the optical amplifier was measured under CW operation using the same bias current indicated in Table1 . Gain measurement were made using the two different amplifier sections to characterize laser performance without an amplifier and with one active amplifier section.... ..."

"... In PAGE 5: ... 3.3 Algorithms Since the problem in Table2 has non-convex (quartic) constraints in Eq. 5 and 6, it is difficult to solve.... In PAGE 5: ... 5 and 6, it is difficult to solve. We consider three approaches inspired from the techniques for non-hierarchical controllers: Non-convex optimization: Use a general non-linear solver, such as SNOPT, to directly tackle the optimization problem in Table2 . This is the most convenient approach, however a globally optimal solution may not be found due to the non-convex nature of the problem.... In PAGE 7: ... 4 Experiments We report on some preliminary experiments with three toy problems (paint, shuttle and maze) from the POMDP repository3. We used the SNOPT package to directly solve the non-convex optimization problem in Table2 and bounded hierarchical policy iteration (BHPI) to solve it iteratively. Table 3 reports the running time and the value of the hierarchical policies found.... ..."

"... In PAGE 5: ... 3.3 Algorithms Since the problem in Table2 has non-convex (quartic) constraints in Eq. 5 and 6, it is difficult to solve.... In PAGE 5: ... 5 and 6, it is difficult to solve. We consider three approaches inspired from the techniques for non-hierarchical controllers: Non-convex optimization: Use a general non-linear solver, such as SNOPT, to directly tackle the optimization problem in Table2 . This is the most convenient approach, however a globally optimal solution may not be found due to the non-convex nature of the problem.... In PAGE 7: ... 4 Experiments We report on some preliminary experiments with three toy problems (paint, shuttle and maze) from the POMDP repository3. We used the SNOPT package to directly solve the non-convex optimization problem in Table2 and bounded hierarchical policy iteration (BHPI) to solve it iteratively. Table 3 reports the running time and the value of the hierarchical policies found.... ..."

"... In PAGE 5: ... 3.3 Algorithms Since the problem in Table2 has non-convex (quartic) constraints in Eq. 5 and 6, it is difficult to solve.... In PAGE 5: ... 5 and 6, it is difficult to solve. We consider three approaches inspired from the techniques for non-hierarchical controllers: Non-convex optimization: Use a general non-linear solver, such as SNOPT, to directly tackle the optimization problem in Table2 . This is the most convenient approach, however a globally optimal solution may not be found due to the non-convex nature of the problem.... In PAGE 7: ... 4 Experiments We report on some preliminary experiments with three toy problems (paint, shuttle and maze) from the POMDP repository3. We used the SNOPT package to directly solve the non-convexoptimization problem in Table2 and bounded hierarchical policy iteration (BHPI) to solve it iteratively. Table 3 reports the running time and the value of the hierarchical policies found.... ..."

"... In PAGE 5: ... A possible method to overcome this limitation would be to widen the layout range and add more patches so that a larger design space could be explored. Table5 : Genetic Algorithm Parameters Genetic Algorithm Parameters Value Crossover Probability 0.65 Mutation Probability 0.... ..."

"... In PAGE 6: ... When an intermediate node receives the same request message again with a larger sequence number, it adjusts (lowers) its Thr by d to allow forwarding to continue. Table3 describes the basic operation behavior of the LEAR algorithm. Table 3.... ..."