By Dimitri P. Bertsekas
This hugely acclaimed paintings, first released via Prentice corridor in 1989, is a accomplished and theoretically sound remedy of parallel and allotted numerical tools. It specializes in algorithms which are evidently fitted to mammoth parallelization, and it explores the elemental convergence, fee of convergence, verbal exchange, and synchronization concerns linked to such algorithms. this is often an in depth ebook, which other than its specialise in parallel and disbursed algorithms, encompasses a wealth of fabric on a vast number of computation and optimization subject matters. between its specific good points, the booklet: 1) Quantifies the functionality of parallel algorithms, together with the constraints imposed by means of the verbal exchange and synchronization consequences. 2) Describes verbal exchange algorithms for numerous approach architectures together with tree, mesh, and hypercube. three) offers a accomplished convergence research of asynchronous equipment and a comparability with their asynchronous opposite numbers. four) Covers direct and iterative algorithms for linear and nonlinear platforms of equations and variational inequalities. five) Describes optimization tools for nonlinear programming, shortest paths, dynamic programming, community flows, and large-scale decomposition. 6) contains huge study fabric on optimization tools, asynchronous set of rules convergence, rollback synchronization, asynchronous communique community protocols, and others. 7) supplementations the textual content fabric with many workouts, whose entire suggestions are published on the net. eight) includes a lot of fabric no longer present in the other e-book.
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Additional info for Parallel and Distributed Computation: Numerical Methods (Optimization and Neural Computation)
Example text
These equations can be solved for C2: C2 = C1 . 2πR2 ⋅ f u 25 This value of C2 will yield a phase margin of about 45 degrees. Increasing the capacitor by a factor of 2 increases the phase margin to about 65 degrees (see References 4 and 5). In practice, the optimum value of C2 may be optimized experimentally by varying it slightly to optimize the output pulse response. 25. In this case, however, the low inverting input impedance, Ro, greatly reduces the sensitivity to input capacitance. In fact, an ideal CFB with zero input impedance would be totally insensitive to any amount of input capacitance!
Note that the intersection of the CFB current noise gain with the open-loop T(s) occurs where the slope of the T(s) function is 12dB/octave. This indicates instability and possible oscillation. It is for this reason that CFB op amps are not suitable in configurations which require capacitance in the feedback loop, such as simple active integrators or lowpass filters. 22 which do not require capacitance in the feedback network. 22 a VFB op amps, on the other hand, make very flexible active filters.
For G = –2, however, 5pF of additional inverting input capacitance causes only a small increase in bandwidth and no significant increase in peaking. High speed VFB op amps are sensitive to stray inverting input capacitance when used in either the inverting or non-inverting mode. 3 DRIVING CAPACITIVE LOADS From system and signal fidelity points of view, transmission line coupling between stages is best, and is described in some detail in the next section. However, complete transmission line system design may not always be possible or practical.
