Search results
Results from the WOW.Com Content Network
Redefining events to downplay their significance can be an effective way of preserving one's self-esteem. [12] One of the problems of depression (found in those with clinical, bipolar, and chronic depressive mood disorders, as well as cyclothymia) is the tendency to do the reverse: minimising the positive, discounting praise, [13] and dismissing one's own accomplishments. [14]
Minimisation or minimization may refer to: . Minimisation (psychology), downplaying the significance of an event or emotion Minimisation (clinical trials) Minimisation (code) or Minification, removing unnecessary characters from source code
This represents the value (or values) of the argument x in the interval (−∞,−1] that minimizes (or minimize) the objective function x 2 + 1 ...
In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function with respect to some variables in the presence of constraints on those variables.
The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...
While these other theories would also support minimizing suffering, they would give special weight to reducing the suffering of those who are in the worse position. The term "negative utilitarianism" is used by some authors to denote the theory that reducing negative well-being is the only thing that ultimately matters morally. [ 4 ]
Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L 1 norm of such values.
The minimax regret approach is to minimize the worst-case regret, originally presented by Leonard Savage in 1951. [16] The aim of this is to perform as closely as possible to the optimal course. Since the minimax criterion applied here is to the regret (difference or ratio of the payoffs) rather than to the payoff itself, it is not as ...