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CUTEr (and SifDec), a constrained and unconstrained testing environment, revisited
 ACM Transactions on Mathematical Software
, 2001
"... Abstract. The initial release of CUTE, a widely used testing environment for optimization software was described in [2]. The latest version, now known as CUTEr is presented. New features include reorganisation of the environment to allow simultaneous multiplatform installation, new tools for, and i ..."
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Cited by 53 (2 self)
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Abstract. The initial release of CUTE, a widely used testing environment for optimization software was described in [2]. The latest version, now known as CUTEr is presented. New features include reorganisation of the environment to allow simultaneous multiplatform installation, new tools for, and interfaces to, optimization packages, and a considerably simplified and entirely automated installation procedure for unix systems. The SIF decoder, which used to be a part of CUTE, has become a separate tool, easily callable by various packages. It features simple extensions to the SIF test problem format and the generation of files suited to automatic differentiation packages. Key words. Nonlinear constrained optimization, testing environment, shared filesystems, heterogeneous environment, SIF format 1.
Separable Nonlinear Least Squares: the Variable Projection Method and its Applications
 Institute of Physics, Inverse Problems
, 2002
"... this paper nonlinear data fitting problems which have as their underlying model a linear combination of nonlinear functions. More generally, one can also consider that there are two sets of unknown parameters, where one set is dependent on the other and can be explicitly eliminated. Models of this t ..."
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Cited by 50 (1 self)
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this paper nonlinear data fitting problems which have as their underlying model a linear combination of nonlinear functions. More generally, one can also consider that there are two sets of unknown parameters, where one set is dependent on the other and can be explicitly eliminated. Models of this type are very common and we will show a variety of applications in different fields. Inasmuch as many inverse problems can be viewed as nonlinear data fitting problems, this material will be of interest to a wide crosssection of researchers and practitioners in parameter, material or system identification, signal analysis, the analysis of spectral data, medical and biological imaging, neural networks, robotics, telecommunications and model order reduction, to name a few
UncertaintyAware Circuit Optimization
 IN DAC
, 2002
"... Almost by definition, welltuned digital circuits have a large number of equally critical paths, which form a socalled "wall" in the slack histogram. However, by the time the design has been through manufacturing, many uncertainties cause these carefully aligned delays to spread out. Inaccuracies i ..."
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Cited by 19 (1 self)
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Almost by definition, welltuned digital circuits have a large number of equally critical paths, which form a socalled "wall" in the slack histogram. However, by the time the design has been through manufacturing, many uncertainties cause these carefully aligned delays to spread out. Inaccuracies in parasitic predictions, clock slew, modeltohardware correlation, static timing assumptions and manufacturing variations all cause the performance to vary from prediction. Simple statistical principles tell us that the variation of the limiting slack is larger when the height of the wall is greater. Although the wall may be the optimum solution if the static timing predictions were perfect, in the presence of uncertainty in timing and manufacturing, it may no longer be the best choice. The application of formal mathematical optimization in transistor sizing increases the height of the wall, thus exacerbating the problem. There is also a practical matter that schematic restructuring downstream in the design methodology is easier to conceive when there are fewer equally critical paths. This paper describes a method that gives formal mathematical optimizers the incentive to avoid the wall of equally critical paths, while giving up as little as possible in nominal performance. Surprisingly, such a formulation reduces the degeneracy of the optimization problem and can render the optimizer more effective. This "uncertaintyaware" mode has been implemented and applied to several highperformance microprocessor macros. Numerical results are included.
Analysis of Inexact TrustRegion SQP Algorithms
 RICE UNIVERSITY, DEPARTMENT OF
, 2000
"... In this paper we extend the design of a class of compositestep trustregion SQP methods and their global convergence analysis to allow inexact problem information. The inexact problem information can result from iterative linear systems solves within the trustregion SQP method or from approximatio ..."
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Cited by 17 (2 self)
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In this paper we extend the design of a class of compositestep trustregion SQP methods and their global convergence analysis to allow inexact problem information. The inexact problem information can result from iterative linear systems solves within the trustregion SQP method or from approximations of firstorder derivatives. Accuracy requirements in our trustregion SQP methods are adjusted based on feasibility and optimality of the iterates. Our accuracy requirements are stated in general terms, but we show how they can be enforced using information that is already available in matrixfree implementations of SQP methods. In the absence of inexactness our global convergence theory is equal to that of Dennis, ElAlem, Maciel (SIAM J. Optim., 7 (1997), pp. 177207). If all iterates are feasible, i.e., if all iterates satisfy the equality constraints, then our results are related to the known convergence analyses for trustregion methods with inexact gradient information fo...
Noise Considerations in Circuit Optimization
 In Proc. International Conference on ComputerAided Design
, 1998
"... Noise can cause digital circuits to switch incorrectly and thus produce spurious results. Noise can also have adverse power, timing and reliability e ects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise ..."
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Cited by 13 (0 self)
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Noise can cause digital circuits to switch incorrectly and thus produce spurious results. Noise can also have adverse power, timing and reliability e ects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise considerations into account. Such considerations are typically stated as semiin nite constraints. In addition, the number of signals to be checked and the number of subintervals of time during which the checking must be performed can potentially be very large. Thus, the practical incorporation of noise constraints during circuit optimization is a hitherto unsolved problem. This paper describes a novel method for incorporating noise considerations during automatic circuit optimization. Semiin nite constraints representing noise considerations are rst converted toordinary equality constraints involving time integrals, which are readily computed in the context of circuit optimization based on timedomain simulation. Next, the gradients of these integrals are computed by the adjoint method. By using an augmented Lagrangian optimization merit function, the adjoint method is applied tocompute all the necessary gradients required for optimization in a single adjoint analysis, no matter how many noise measurements are considered and irrespective of the dimensionality of the problem. Numerical results are presented. 1
Circuit Optimization via Adjoint Lagrangians
 IEEE INTERNATIONAL CONFERENCE ON COMPUTERAIDED DESIGN
, 1997
"... The circuit tuning problem is best approached by means of gradientbased nonlinear optimization algorithms. For large circuits, gradient computation can be the bottleneck in the optimization procedure. Traditionally, when the number of measurements is large relative to the number of tunable paramete ..."
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Cited by 6 (3 self)
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The circuit tuning problem is best approached by means of gradientbased nonlinear optimization algorithms. For large circuits, gradient computation can be the bottleneck in the optimization procedure. Traditionally, when the number of measurements is large relative to the number of tunable parameters, the direct method [2] is used to repeatedly solve the associated sensitivity circuit to obtain all the necessary gradients. Likewise, when the parameters outnumber the measurements, the adjoint method [1] is employed to solve the adjoint circuit repeatedly for each measurement to compute the sensitivities. In this paper, we propose the adjoint Lagrangian method, which computes all the gradients necessary for augmentedLagrangianbased optimization in a single adjoint analysis. After the nominal simulation of the circuit has been carried out, the gradients of the merit function are expressed as the gradients of a weighted sum of circuit measurements. The weights are dependent on the nominal solution and on optimizer quantities such as Lagrange multipliers. By suitably choosing the excitations of the adjoint circuit, the gradients of the merit function are computed via a single adjoint analysis, irrespective of the number of measurements and the number of parameters of the optimization. This procedure requires close integration between the nonlinear optimization software and the circuit simulation program. The adjoint
LargeScale Nonlinear Optimization in Circuit Tuning
, 2003
"... Circuit tuning is an important task in the design of custom digital integrated circuits such as highperformance microprocessors. The goal is to improve certain aspects of the circuit, such as speed, area, or power, by optimally choosing the sizes of the transistors. This task can be formulated as a ..."
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Cited by 3 (1 self)
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Circuit tuning is an important task in the design of custom digital integrated circuits such as highperformance microprocessors. The goal is to improve certain aspects of the circuit, such as speed, area, or power, by optimally choosing the sizes of the transistors. This task can be formulated as a largescale nonlinear, nonconvex optimization problem, where function values and derivatives are obtained by simulation of individual gates. This application o#ers an excellent example of a nonlinear optimization problem, for which it is very desirable to increase the size of the problems that can be solved in a reasonable amount of time. In this paper we describe the mathematical formulation of this problem and the implementation of a circuit tuning tool. We demonstrate how the integration of a novel stateoftheart interior point algorithm for nonlinear programming led to considerable improvement in e# ciency and robustness. Particularly, as will be demonstrated with numerical results, the new approach has great potential for parallel and distributed computing.
TOULOUSE (FRANCE) Spécialité: Mathématiques, Informatique et Télécommunications et de
"... vue de l'obtention du ..."
Noise Considerations in . . .
"... Noise can cause digital circuits to switch incorrectly, producing spurious results. It can also have adverse power, timing and reliability effects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise consider ..."
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Noise can cause digital circuits to switch incorrectly, producing spurious results. It can also have adverse power, timing and reliability effects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise considerations into account. Such considerations are typically stated as semiinfinite constraints in the timedomain. Semiinfinite problems are generally harder to solve than standard nonlinear optimization problems. Moreover, the number of noise constraints can potentially be very large. This paper