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15
Optimal design of a CMOS opamp via geometric programming
 IEEE Transactions on ComputerAided Design
, 2001
"... We describe a new method for determining component values and transistor dimensions for CMOS operational ampli ers (opamps). We observe that a wide variety of design objectives and constraints have a special form, i.e., they are posynomial functions of the design variables. As a result the ampli er ..."
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Cited by 51 (10 self)
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We describe a new method for determining component values and transistor dimensions for CMOS operational ampli ers (opamps). We observe that a wide variety of design objectives and constraints have a special form, i.e., they are posynomial functions of the design variables. As a result the ampli er design problem can be expressed as a special form of optimization problem called geometric programming, for which very e cient global optimization methods have been developed. As a consequence we can e ciently determine globally optimal ampli er designs, or globally optimal tradeo s among competing performance measures such aspower, openloop gain, and bandwidth. Our method therefore yields completely automated synthesis of (globally) optimal CMOS ampli ers, directly from speci cations. In this paper we apply this method to a speci c, widely used operational ampli er architecture, showing in detail how to formulate the design problem as a geometric program. We compute globally optimal tradeo curves relating performance measures such as power dissipation, unitygain bandwidth, and openloop gain. We show how the method can be used to synthesize robust designs, i.e., designs guaranteed to meet the speci cations for a
Radioptimization  Goal Based Rendering
 In Computer Graphics Proceedings, Annual Conference Series
, 1993
"... This paper presents a method for designing the illumination in an environment using optimization techniques applied to a radiosity based image synthesis system. An optimization of lighting parameters is performed based on user specified constraints and objectives for the illumination of the envir ..."
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Cited by 42 (0 self)
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This paper presents a method for designing the illumination in an environment using optimization techniques applied to a radiosity based image synthesis system. An optimization of lighting parameters is performed based on user specified constraints and objectives for the illumination of the environment. The system solves for the "best" possible settings for: light source emissivities, element reflectivities, and spot light directionality parameters so that the design goals, suchastominimize energy or to give the the room an impression of privacy, are met. The system absorbs much of the burden for searching the design space allowing the user to focus on the goals of the illumination design rather than the intricate details of a complete lighting specification. A software implementation is described and some results of using the system are reported.
Restoration of Services in Interdependent Infrastructure Systems: A Network Flows Approach
 Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute
, 2003
"... Abstract — Modern society depends on the operations of civil infrastructure systems, such as transportation, energy, telecommunications and water. Clearly, disruption of any of these systems would present a significant detriment to daily living. However, these systems have become so interconnected, ..."
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Cited by 7 (2 self)
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Abstract — Modern society depends on the operations of civil infrastructure systems, such as transportation, energy, telecommunications and water. Clearly, disruption of any of these systems would present a significant detriment to daily living. However, these systems have become so interconnected, one relying on another, that disruption of one may lead to disruptions in all. The focus of this research is on developing techniques which can be used to respond to events that have the capability to impact interdependent infrastructure systems. As discussed in the paper, infrastructure interdependencies occur when, due to either geographical proximity or shared operations, an impact on one infrastructure system affects one or more other infrastructure systems. The approach is to model the salient elements of these systems and provide decision makers with a means to manipulate the set of models, i.e. a decision support system. 1
A Relational Approach To Optimization Problems
, 1996
"... The main contribution of this thesis is a study of the dynamic programming and greedy strategies for solving combinatorial optimization problems. The study is carried out in the context of a calculus of relations, and generalises previous work by using a loop operator in the imperative programming s ..."
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Cited by 6 (0 self)
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The main contribution of this thesis is a study of the dynamic programming and greedy strategies for solving combinatorial optimization problems. The study is carried out in the context of a calculus of relations, and generalises previous work by using a loop operator in the imperative programming style for generating feasible solutions, rather than the fold and unfold operators of the functional programming style. The relationship between fold operators and loop operators is explored, and it is shown how to convert from the former to the latter. This fresh approach provides additional insights into the relationship between dynamic programming and greedy algorithms, and helps to unify previously distinct approaches to solving combinatorial optimization problems. Some of the solutions discovered are new and solve problems which had previously proved difficult. The material is illustrated with a selection of problems and solutions that is a mixture of old and new. Another contribution is the invention of a new calculus, called the graph calculus, which is a useful tool for reasoning in the relational calculus and other nonrelational calculi. The graph
Dynamic Programming: a different perspective
 Algorithmic Languages and Calculi
, 1997
"... Dynamic programming has long been used as an algorithm design technique, with various mathematical theories proposed to model it. Here we take a different perspective, using a relational calculus to model the problems and solutions using dynamic programming. This approach serves to shed new light on ..."
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Cited by 5 (0 self)
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Dynamic programming has long been used as an algorithm design technique, with various mathematical theories proposed to model it. Here we take a different perspective, using a relational calculus to model the problems and solutions using dynamic programming. This approach serves to shed new light on the different styles of dynamic programming, representing them by different search strategies of the treelike space of partial solutions. 1 INTRODUCTION AND HISTORY Dynamic programming is an algorithm design technique for solving many different types of optimization problem, applicable to such diverse fields as operations research (Ecker and Kupferschmid, 1988) and neutron transport theory (Bellman, Kagiwada and Kalaba, 1967). The mathematical theory of the subject dates back to 1957, when Richard Bellman (Bellman, 1957) first popularized the idea, producing a mathematical theory to model multistage decision processes and to solve related optimization problems. He was also the first to i...
Between Dynamic Programming and Greedy: Data Compression
 Programming Research Group, 11 Keble Road, Oxford OX1 3QD
, 1995
"... The derivation of certain algorithms can be seen as a hybrid form of dynamic programming and the greedy paradigm. We present a generic theorem about such algorithms, and show how it can be applied to the derivation of an algorithm for data compression. 1 Introduction Dynamic programming is a techni ..."
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Cited by 5 (0 self)
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The derivation of certain algorithms can be seen as a hybrid form of dynamic programming and the greedy paradigm. We present a generic theorem about such algorithms, and show how it can be applied to the derivation of an algorithm for data compression. 1 Introduction Dynamic programming is a technique for solving optimisation problems. A typical dynamic programming algorithm proceeds by decomposing the input in all possible ways, recursively solving the subproblems, and combining optimal solutions to subproblems into an optimal solution for the whole problem. The greedy paradigm is also a technique for solving optimisation problems and differs from dynamic programming in that only one decomposition of the input is considered. Such a decomposition is usually chosen to maximise some objective function, and this explains the term `greedy'. In our earlier work, we have characterised the use of dynamic programming and the greedy paradigm, using the categorical calculus of relations to der...
Attacks and accidents: Policy to protect the power grid’s critical computing and communication needs. Senior interdisciplinary honors thesis in international security studies
, 2004
"... The most likely way for the world to be destroyed, most experts agree, is by accident. That’s where we come in; we’re computer professionals. We cause accidents. Nathaniel Borenstein1 Q. So just put it in all perspective [sic]. What’s the worstcase power scenario, power we’re talking here – power ..."
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Cited by 4 (0 self)
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The most likely way for the world to be destroyed, most experts agree, is by accident. That’s where we come in; we’re computer professionals. We cause accidents. Nathaniel Borenstein1 Q. So just put it in all perspective [sic]. What’s the worstcase power scenario, power we’re talking here – power lines, power grid? A. Absolute worst? I won’t even say absolute, but a very worst case could be loss of power for six months or more. Q. Over how big an area? A. Big as you want. Frontline interview with power expert Joseph Weiss regarding the possible damage of a cyber attack on the power grid. 2
Computing the radius of positive semidefiniteness of a multivariate real polynomial via a dual of Seidenberg’s method
, 2010
"... ..."
M.J.: Fuzzygenetic decision optimization for optimization of complex stochastic systems
 In: Proceedings 5th Online World Conference on Soft Computing in Industrial Applications. (2000
"... multiobjective optimization on complex stochastic systems. A stochastic simulation model estimates the results of parameter settings for the system. A fuzzy ordinal preference model aggregates these results into a single fitness value for the input parameter set. A genetic algorithm uses this fitne ..."
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Cited by 1 (0 self)
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multiobjective optimization on complex stochastic systems. A stochastic simulation model estimates the results of parameter settings for the system. A fuzzy ordinal preference model aggregates these results into a single fitness value for the input parameter set. A genetic algorithm uses this fitness value to search for a population of high performance parameter sets which achieve the modeler’s objectives. In a tactical planning experiment, FuzzyGenetic Decision Optimization enabled a human planner to develop a significantly better plan than he developed without automated assistance. 1