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The rank-based fit of a linear model is based on minimizing a norm.
The gain matrix is estimated based on minimizing a performance criterion function.
The HNGPS-CA method is based on minimizing a constraint objective function to estimate the MS location.
Conventional ophthalmic lens design is based on minimizing a merit function with the help of optimization algorithms.
The coefficients are selected based on minimizing a cost function that combines both distortion and bit rate via a user-controlled weighting.
We describe here an iterative parameter optimization strategy that is based on minimizing a structural error measure by descent in parameter space.
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This optimization was based on minimizing an error function given by the difference between the experimental and analytical frequencies.
These methods are based on minimizing an upper bound of the sum of the block-coherences of the sensing matrix blocks.
Model selection is based on minimizing an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.
Most packages share the concept of parameter estimation based on minimizing an objective function value (OFV), often using maximum likelihood estimation.
We present an algorithm for quantitative photoacoustic estimation of optical absorption and diffusion coefficients based on minimizing an error function between measured photoacoustic channel data and a calculated forward model with a multiple-illumination pattern.
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