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Three objectives are considered: minimizing the sum of squares of distances, minimizing the maximum distance, and minimizing the sum of distances.
We study a distributed multi-agent optimization problem of minimizing the sum of convex objective functions.
Minimizing the sum of investment and operating costs (De Wolf and Smeers 2012). 5.
This problem recovers a color image by minimizing the sum of differences between neighbor pixels.
Classical LS regression consists of minimizing the sum of the squared residuals.
Then it obtains parameter estimates by minimizing the sum of remaining (trimmed) squares of residuals.
In [4] iterative distributed DSM algorithms are proposed for minimizing the sum power for each user.
It allows to determine the regression coefficients by minimizing the sum of squared residuals (deviations).
These parameters are estimated by minimizing the sum of squares of differences between the observed and simulated concentration fields.
The estimation is done by minimizing the sum of the squares of the errors between the model and measured data.
Some authors predict unmeasured values (flowrate, oil fraction…) simply by minimizing the sum of the residue of each component [12].
More suggestions(15)
reduced the sum
limit the sum
minimizing the entropy
minimizing the threat
minimizing the risk
minimizing the query
minimizing the processing
minimizing the level
minimizing the impact
minimizing the number
minimizing the damage
minimizing the attrition
minimizing the incidence
minimizing the embarrassment
minimizing the energy
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com