Results 1  10
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45
Newton's Method For Large BoundConstrained Optimization Problems
 SIAM JOURNAL ON OPTIMIZATION
, 1998
"... We analyze a trust region version of Newton's method for boundconstrained problems. Our approach relies on the geometry of the feasible set, not on the particular representation in terms of constraints. The convergence theory holds for linearlyconstrained problems, and yields global and super ..."
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Cited by 82 (4 self)
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We analyze a trust region version of Newton's method for boundconstrained problems. Our approach relies on the geometry of the feasible set, not on the particular representation in terms of constraints. The convergence theory holds for linearlyconstrained problems, and yields global and superlinear convergence without assuming neither strict complementarity nor linear independence of the active constraints. We also show that the convergence theory leads to an efficient implementation for large boundconstrained problems.
Optimal Decoupling Capacitor Sizing and Placement for Standard Cell Layout Designs
 IEEE Trans. on ComputerAided Design of Integrated Circuits and Systems
, 1995
"... With technology scaling, the trend for high performance integrated circuits is towards ever higher operating frequency, lower power supply voltages and higher power dissipation. ..."
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Cited by 52 (4 self)
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With technology scaling, the trend for high performance integrated circuits is towards ever higher operating frequency, lower power supply voltages and higher power dissipation.
Fast sweeping methods for static hamiltonjacobi equations
 Society for Industrial and Applied Mathematics
, 2005
"... Abstract. We propose a new sweeping algorithm which discretizes the Legendre transform of the numerical Hamiltonian using an explicit formula. This formula yields the numerical solution at a grid point using only its immediate neighboring grid values and is easy to implement numerically. The minimiz ..."
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Cited by 43 (4 self)
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Abstract. We propose a new sweeping algorithm which discretizes the Legendre transform of the numerical Hamiltonian using an explicit formula. This formula yields the numerical solution at a grid point using only its immediate neighboring grid values and is easy to implement numerically. The minimization that is related to the Legendre transform in our sweeping scheme can either be solved analytically or numerically. We illustrate the efficiency and accuracy approach with several numerical examples in two and three dimensions.
Using model knowledge for learning inverse dynamics
 In Proc. IEEE International Conference on Robotics and Automation
, 2010
"... Abstract — In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in most robot learning approaches, the model is learned from scratch disregarding all prior knowledge about the syst ..."
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Cited by 17 (2 self)
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Abstract — In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in most robot learning approaches, the model is learned from scratch disregarding all prior knowledge about the system. For many complex robot systems, available prior knowledge from advanced physicsbased modeling techniques can entail valuable information for model learning that may result in faster learning speed, higher accuracy and better generalization. In this paper, we investigate how parametric physical models (e.g., obtained from rigid body dynamics) can be used to improve the learning performance, and, especially, how semiparametric regression methods can be applied in this context. We present two possible semiparametric regression approaches, where the knowledge of the physical model can either become part of the mean function or of the kernel in a nonparametric Gaussian process regression. We compare the learning performance of these methods first on sampled data and, subsequently, apply the obtained inverse dynamics models in tracking control on a real Barrett WAM. The results show that the semiparametric models learned with rigid body dynamics as prior outperform the standard rigid body dynamics models on real data while generalizing better for unknown parts of the state space. I.
Keyframe control of complex particle systems using the adjoint method
, 2006
"... Control of physical simulation has become a popular topic in the field of computer graphics. Keyframe control has been applied to simulations of rigid bodies, smoke, liquid, flocks, and finite elementbased elastic bodies. In this paper, we create a framework for controlling systems of interacting p ..."
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Cited by 17 (0 self)
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Control of physical simulation has become a popular topic in the field of computer graphics. Keyframe control has been applied to simulations of rigid bodies, smoke, liquid, flocks, and finite elementbased elastic bodies. In this paper, we create a framework for controlling systems of interacting particles – paying special attention to simulations of cloth and flocking behavior. We introduce a novel integratorswapping approximation in order to apply the adjoint method to linearized implicit schemes appropriate for cloth simulation. This allows the control of cloth while avoiding computationally infeasible derivative calculations. Meanwhile, flocking control using the adjoint method is significantly more efficient than currentlyused methods for constraining group behaviors, allowing the controlled simulation of greater numbers of agents in fewer optimization iterations. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Physically based modeling; I.3.7 [Computer Graphics]: Animation; I.6.8 [Simulation and Modeling]: Animation;
Attractorrepeller approach for global placement
 In Proc. IEEE/ACM Intl. Conf. on ComputerAided Design
, 1999
"... Traditionally, analytic placement used linear or quadratic wirelength objective functions. Minimizing either formulation attracts cells sharing common signals (nets) together. The result is a placement with a great deal of overlap among the cells. To reduce cell overlap, the methodology iterates be ..."
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Cited by 17 (2 self)
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Traditionally, analytic placement used linear or quadratic wirelength objective functions. Minimizing either formulation attracts cells sharing common signals (nets) together. The result is a placement with a great deal of overlap among the cells. To reduce cell overlap, the methodology iterates between global optimization and repartitioning of the placement area. In this work, we added new attractive and repulsive forces to the traditional formulation so that overlap among cells is diminished without repartitioning the placement area. The superiority of our approach stems from the fact that our new formulations are convex and no hard constraints are required. A preliminary version of the new placement method is tested using a set of MCNC benchmarks 1 and, on average, the new method achieved and reduction in wirelength and CPU time compared to TimberWolf v7.0 in hierarchical mode [10]. 1.
The NetworkEnabled Optimization System (NEOS) Server
, 1996
"... The NetworkEnabled Optimization System (NEOS) is an environment for solving optimization problems over the Internet. Users submit optimization problems to the NEOS Server via email, the World Wide Web, or the NEOS Submission Tool. The NEOS Server locates the appropriate optimization solver, comput ..."
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Cited by 11 (1 self)
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The NetworkEnabled Optimization System (NEOS) is an environment for solving optimization problems over the Internet. Users submit optimization problems to the NEOS Server via email, the World Wide Web, or the NEOS Submission Tool. The NEOS Server locates the appropriate optimization solver, computes all additional information (for example, derivatives and sparsity patterns) required by the solver, links the optimization problem with the solver, and returns a solution. This article discusses the design and implementation of the NEOS Server.
Accelerated Monotonic Algorithms for Transmission Tomography
 In Proc. IEEE Intl. Conf. on Image Processing
, 1998
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Kinematic Control Of Human Postures For Task Simulation
, 1996
"... Kinematic control of human postures for task simulation is important in human factor analysis, simulation and training. It is a challenge to control the postures of a synthesized human figure in realtime on today's graphics workstations because the human body is highly articulated. In additio ..."
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Cited by 8 (0 self)
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Kinematic control of human postures for task simulation is important in human factor analysis, simulation and training. It is a challenge to control the postures of a synthesized human figure in realtime on today's graphics workstations because the human body is highly articulated. In addition, we need to consider many spatial restrictions imposed on the human body while performing a task. In this study, we simplify the human posture control problem by decoupling the degrees of freedom (dof) in the human body. Based on several decoupling schemes, we develop an analytical human posture control algorithm. This analytical algorithm has a number of advantages over existing methods. It eliminates the local minima problem, it is efficient enough to control whole human body postures in realtime, and it provides more effective and convenient control over redundant degrees of freedom. The limitation of this a...
Frequency domain optical tomography based on the equation of radiative transfer
 SIAM J. Sci. Comput
"... Abstract. Optical tomography consists of reconstructing the spatial distribution of absorption and scattering properties of a medium from surface measurements of transmitted light intensities. Mathematically, this problem amounts to parameter identification for the equation of radiative transfer (ER ..."
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Cited by 7 (4 self)
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Abstract. Optical tomography consists of reconstructing the spatial distribution of absorption and scattering properties of a medium from surface measurements of transmitted light intensities. Mathematically, this problem amounts to parameter identification for the equation of radiative transfer (ERT) with diffusiontype boundary measurements. Because they are posed in the phasespace, radiative transfer equations are quite challenging to solve computationally. Most past works have considered the steadystate ERT or the diffusion approximation of the ERT. In both cases, substantial crosstalk has been observed in the reconstruction of the absorption and scattering properties of inclusions. In this paper, we present an optical tomographic reconstruction algorithm based on the frequencydomain ERT. The inverse problem is formulated as a regularized leastsquares minimization problem, in which the mismatch between forward model predictions and measurements is minimized. The ERT is discretized by using a discrete ordinates method for the directional variables and a finite volume method for the spatial variables. A limitedmemory quasiNewton algorithm is used to minimize the leastsquares functional. Numerical simulations with synthetic data show that the crosstalk between the two optical parameters is significantly reduced in reconstructions based on frequencydomain data as compared to those based on steadystate data. Key words. optical tomography, photon density waves, equation of radiative transfer, finite volume method, discrete ordinates method, generalized minimal residual algorithm, inverse problems,