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22
Synthesis of highperformance analog circuits in astrx/oblx
 IEEE Trans. CAD
, 1996
"... Abstract We present a new synthesis strategy that can automate fully the path from an analog circuit topology and performance specifications to a sized circuit schematic. This strategy relies on asymptotic waveform evaluation to predict circuit performance and simulated annealing to solve a nove ..."
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Cited by 72 (5 self)
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Abstract We present a new synthesis strategy that can automate fully the path from an analog circuit topology and performance specifications to a sized circuit schematic. This strategy relies on asymptotic waveform evaluation to predict circuit performance and simulated annealing to solve a novel unconstrained optimization formulation of the circuit synthesis problem. We have implemented this strategy in a pair of tools called ASTRX and OBLX. To show the generality of our new approach, we have used this system to resynthesize essentially all the analog synthesis benchmarks published in the past decade; ASTWOBLX has resynthesized circuits in an afternoon that, for some prior approaches, had required months. To show the viability of the approach on difficult circuits, we have resynthesized a recently published (and patented), highperformance operational amplifier; ASTWOBLX achieved performance comparable to the expert manual design. And finally, to test the limits of the approach on industrialsized problems, we have synthesized the component cells of a pipelined A/D converter; ASTWOBLX successfully generated cells 23 x more complex than those published previously. I.
Evaluating the Scalability of Distributed Systems
 IEEE Transactions on Parallel and Distributed Systems
, 2000
"... AbstractÐMany distributed systems must be scalable, meaning that they must be economically deployable in a wide range of sizes and configurations. This paper presents a scalability metric based on costeffectiveness, where the effectiveness is a function of the system's throughput and its quali ..."
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Cited by 63 (2 self)
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AbstractÐMany distributed systems must be scalable, meaning that they must be economically deployable in a wide range of sizes and configurations. This paper presents a scalability metric based on costeffectiveness, where the effectiveness is a function of the system's throughput and its quality of service. It is part of a framework which also includes a scaling strategy for introducing changes as a function of a scale factor, and an automated virtual design optimization at each scale factor. This is an adaptation of concepts for scalability measures in parallel computing. Scalability is measured by the range of scale factors that give a satisfactory value of the metric, and good scalability is a joint property of the initial design and the scaling strategy. The results give insight into the scaling capacity of the designs, and into how to improve the design. A rapid simple bound on the metric is also described. The metric is demonstrated in this work by applying it to some wellknown idealized systems, and to real prototypes of communications software. Index TermsÐScalability, distributed systems, scalability metric, software performance, performance model, layered queuing, performance optimization, replication. 1
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
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Cited by 55 (6 self)
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A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
Simulated Annealing Algorithms For Continuous Global Optimization
, 2000
"... INTRODUCTION In this paper we consider Simulated Annealing algorithms (SA in what follows) applied to continuous global optimization problems, i.e. problems with the following form f = min x2X f(x); (1.1) where X ` ! n is a continuous domain, often assumed to be compact, which, combined with ..."
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Cited by 47 (1 self)
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INTRODUCTION In this paper we consider Simulated Annealing algorithms (SA in what follows) applied to continuous global optimization problems, i.e. problems with the following form f = min x2X f(x); (1.1) where X ` ! n is a continuous domain, often assumed to be compact, which, combined with the continuity or lower semicontinuity of f , guarantees the existence of the minimum value f . SA algorithms are based on an analogy with a physical phenomenon: while at high temperatures the molecules in a liquid move freely, if the temperature is slowly decreased the thermal mobility of the molecules is lost and they form a pure crystal which also corresponds to a state of minimum energy. If the temperature is decreased too quickly (the so called quenching) a liquid metal rather ends up in a polycrystalline or amorphous state with
The Continuous Reactive Tabu Search: Blending Combinatorial Optimization and Stochastic Search for Global Optimization
, 1995
"... A novel algorithm for the global optimization of functions (CRTS) is presented, in which a combinatorial optimization method cooperates with a stochastic local minimizer. The combinatorial optimization component, based on the Reactive Tabu Search recently proposed by the authors, locates the most p ..."
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Cited by 18 (2 self)
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A novel algorithm for the global optimization of functions (CRTS) is presented, in which a combinatorial optimization method cooperates with a stochastic local minimizer. The combinatorial optimization component, based on the Reactive Tabu Search recently proposed by the authors, locates the most promising "boxes," where starting points for the local minimizer are generated. In order to cover a wide spectrum of possible applications with no user intervention, the method is designed with adaptive mechanisms: the box size is adapted to the local structure of the function to be optimized, the search parameters are adapted to obtain a proper balance of diversification and intensification. The algorithm is compared with some existing algorithms, and the experimental results are presented for a suite of benchmark tasks.
Global Optimization For Constrained Nonlinear Programming
, 2001
"... In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary ..."
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Cited by 14 (2 self)
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In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for constrained local minima (CLM dn ) in the theory of discrete constrained optimization using Lagrange multipliers developed in our group. The theory proves the equivalence between the set of discrete saddle points and the set of CLM dn, leading to the firstorder necessary and sufficient condition for CLM dn. To find
Genetic algorithms for finite mixture model based tissue classification
 in brain MRI,” in Proc. Eur. Med. Biol. Eng. Conf. (EMBEC), IFMBE, 2005
"... Abstract—Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectationmaximi ..."
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Cited by 13 (3 self)
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Abstract—Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectationmaximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are twofold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce
Application of the parallel fast messy genetic algorithm to the protein folding problem
 Proceedings of the Intel Supercomputer Users Group Conference
, 1993
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Synthesis of Manufacturable Analog Circuits
 Proceedings of ACM/IEEE ICCAD
"... Abstract † We describe a synthesis system that takes operating range constraints and inter and intra circuit parametric manufacturing variations into account while designing a sized and biased analog circuit. Previous approaches to CAD for analog circuit synthesis have concentrated on nominal anal ..."
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Cited by 10 (6 self)
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Abstract † We describe a synthesis system that takes operating range constraints and inter and intra circuit parametric manufacturing variations into account while designing a sized and biased analog circuit. Previous approaches to CAD for analog circuit synthesis have concentrated on nominal analog circuit design, and subsequent optimization of these circuits for statistical fluctuations and operating point ranges. Our approach simultaneously synthesizes and optimizes for operating and manufacturing variations by mapping the circuit design problem into an Infinite Programming problem and solving it using an annealing within annealing formulation. We present circuits designed by this integrated synthesis system, and show that they indeed meet their operating range and parametric manufacturing constraints. 1