Results 241 to 250 of about 229,971 (268)

General, Nested, and Constrained Wiberg Minimization

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
Wiberg matrix factorization breaks a matrix Y into low-rank factors U and V by solving for V in closed form given U, linearizing V(U) about U, and iteratively minimizing ||Y - UV(U)||2 with respect to U only. This approach factors the matrix while effectively removing V from the minimization.
Strelow, Dennis, Wang, Qifan, Si, Luo, Eriksson, Anders   +3 more
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Constrained energy minimization and the target-constrained interference-minimized filter

Optical Engineering, 2003
This PDF contains the communication "Constrained energy minimization and the target-constrained interference-minimized filter."
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Constrained Minimization: Equality Constraints

2003
The minimization of a function whose variables must satisfy inequality constraints is considered here. Because of their nature, any number of inequality constraints can be imposed. To introduce the subject, conditions for locating a boundary minimal point of a function of one independent variable (see Section 2.2) are determined using the direct ...
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Constrained multiobjective distance minimization problems

Proceedings of the Genetic and Evolutionary Computation Conference, 2019
Various distance minimization problems (DMPs) have been proposed to visualize the search behaviors of evolutionary multiobjective optimization (EMO) algorithms in solving many-objective problems, multiobjective multimodal problems, and dynamic multiobjective problems. Among those DMPs, only the box constraints are considered.
Yusuke Nojima, Takafumi Fukase, Yiping Liu, Naoki Masuyama, Hisao Ishibuchi   +4 more
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Control of minimally constrained cobots

Journal of Robotic Systems, 2002
AbstractCobots are devices which use computer‐oriented passive constraints to guide an end‐effector driven by a human. This synergistic union of human skill and robotic precision is desired in fields such as surgical robotics (our application area of interest) where the surgeon would prefer not to hand over control of a procedure to an autonomous robot.
Hodgson, Antony J., Emrich, Richard
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QoS-Constrained Power Minimization

2012
In this chapter we discuss an algorithmic solution for the QoS-constrained power minimization problem, which was already introduced and discussed in Section 2.8. The following optimization framework is applicable to arbitrary systems of concave or convex standard interference functions \({\mathcal J}_k(p) = {\mathcal I}_k(\underline{p})\) (see Section ...
Martin Schubert, Holger Boche
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CONSTRAINED MULTIVARIATE LOSS FUNCTION MINIMIZATION

Quality Engineering, 2001
Modifying Taguchi's one-dimensional quadratic loss function to a multidimensional form has attracted many researchers because a more accurate estimate of total loss due to variation from multiple quality targets would result. This article includes a..
Richard Suhr, Robert G. Batson
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Computing constrained energy-minimizing flows

2011 3rd International Conference on Computer Research and Development, 2011
Conservative flows that minimize the kinetic energy have be linked to the problem of optimal transport, a field with numerous applications in many areas of mathematics, science and engineering ranging from probability theory, fluid dynamics meteorology, oceanography to antenna design, image registration and shape analysis among others.
Said Kerrache, Yasushi Nakauchi
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Constrained Minimization Using Powell’s Conjugacy Approach

SIAM Journal on Numerical Analysis, 1976
We consider the problem of minimizing a function f of n real variables $x_1 , \cdots ,x_n $, subject to the provision that derivative information is not to be used in seeking the minimum. A number of methods exist for solving this problem and of these, several are based on the construction of conjugate or orthogonal search directions.
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Multiple classifiers by constrained minimization

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002
The paper describes an approach to combining multiple classifiers in order to improve classification accuracy. Since individual classifiers in the ensemble should somehow be uncorrelated to yield higher classification accuracy than a single classifier, we propose to train classifiers by minimizing the correlation between their classification errors.
Niyogi, P., Pierrot, J.-B., Siohan, O.
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