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Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters.
A firm wants to minimize sum of discounted total cost of satisfying demands for a single input.
The main objective of Function (12) is to minimize sum of the total transportation cost for the vehicles of coalition S m.
The method finds the optimal design of the vehicle by iteratively using dynamic programming (DP) and convex optimization to minimize sum of operational and component costs over a given driving cycle.
(10) U_{lk} ge 0,,,forallorall, l in J,, k in K (11)The objective function (1) is to minimize sum of the total transportation cost for the vehicles.
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Lasso applies linear discriminant analysis with minimizing sum of regression coefficients.
We use regression analysis assuming normally distributed errors (minimizing sums of squares) and Poisson distributed errors (maximizing Poisson log-likelihood).
The fit for each model to observed counts of observed waterbirds is performed using regression analysis, minimizing sums of squares by assuming both normally distributed and log-normally distributed errors.
A new method is proposed here that minimizes sum of the total transportation cost for the vehicles of a coalition.
For comparison purposes, MLPs have been trained to minimize the Sum-of-Squares error and the Minkowski error (R = 1).
Rather than minimize the sum of squared residuals for all data points included in ordinary least squares regression, LTS minimizes the sum of squared residuals for a subset of data points that minimizes the sum of squared residuals to remove the influence of outliers from the regression.
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