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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]
This represents the value (or values) of the argument x in the interval (−∞,−1] that minimizes (or minimize) the objective function x 2 + 1 (the actual minimum value of that function is not what the problem asks for).
The MM algorithm is an iterative optimization method which exploits the convexity of a function in order to find its maxima or minima. The MM stands for “Majorize-Minimization” or “Minorize-Maximization”, depending on whether the desired optimization is a minimization or a maximization.
The geometric interpretation of Newton's method is that at each iteration, it amounts to the fitting of a parabola to the graph of () at the trial value , having the same slope and curvature as the graph at that point, and then proceeding to the maximum or minimum of that parabola (in higher dimensions, this may also be a saddle point), see below.
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
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 ...
Under the free energy principle, systems pursue paths of least surprise, or equivalently, minimize the difference between predictions based on their model of the world and their sense and associated perception. This difference is quantified by variational free energy and is minimized by continuous correction of the world model of the system, or ...
A convex optimization problem is defined by two ingredients: [5] [6] The objective function, which is a real-valued convex function of n variables, :;; The feasible set, which is a convex subset.