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  2. Diminishing returns - Wikipedia

    en.wikipedia.org/wiki/Diminishing_returns

    This is called "negative returns". [18] Through each of these examples, the floor space and capital of the factor remained constant, i.e., these inputs were held constant. By only increasing the number of people, eventually the productivity and efficiency of the process moved from increasing returns to diminishing returns.

  3. Internal working model of attachment - Wikipedia

    en.wikipedia.org/wiki/Internal_working_model_of...

    In the latter case, the infant itself might be drawn to construct a negative working model of the self and the relationship. Furthermore, a parent with a negative, poorly organized and inconsistent working model might fail to provide useful feedback about the parent-infant dyad and other relationships, thus disrupting the infant's forming of a ...

  4. Descent direction - Wikipedia

    en.wikipedia.org/wiki/Descent_direction

    In optimization, a descent direction is a vector that points towards a local minimum of an objective function :.. Computing by an iterative method, such as line search defines a descent direction at the th iterate to be any such that , <, where , denotes the inner product.

  5. Levenberg–Marquardt algorithm - Wikipedia

    en.wikipedia.org/wiki/Levenberg–Marquardt...

    The (non-negative) damping factor ⁠ ⁠ is adjusted at each iteration. If reduction of ⁠ S {\displaystyle S} ⁠ is rapid, a smaller value can be used, bringing the algorithm closer to the Gauss–Newton algorithm , whereas if an iteration gives insufficient reduction in the residual, ⁠ λ {\displaystyle \lambda } ⁠ can be increased ...

  6. Gradient descent - Wikipedia

    en.wikipedia.org/wiki/Gradient_descent

    Illustration of gradient descent on a series of level sets. Gradient descent is based on the observation that if the multi-variable function is defined and differentiable in a neighborhood of a point , then () decreases fastest if one goes from in the direction of the negative gradient of at , ().

  7. Double descent - Wikipedia

    en.wikipedia.org/wiki/Double_descent

    Double descent in statistics and machine learning is the phenomenon where a model with a small number of parameters and a model with an extremely large number of parameters both have a small training error, but a model whose number of parameters is about the same as the number of data points used to train the model will have a much greater test ...

  8. Work systems - Wikipedia

    en.wikipedia.org/wiki/Work_systems

    Change management efforts about rationale and positive or negative impacts of changes; Training on details of the new or revised information system and work system; Conversion to the new or revised work system; Acceptance testing; As an example of the iterative nature of a work system's life cycle, consider the sales system in a software start-up.

  9. Hayes-Wheelwright matrix - Wikipedia

    en.wikipedia.org/wiki/Hayes-Wheelwright_matrix

    The Hayes-Wheelwright matrix is a four-stage model; each stage is characterized by the management strategy implemented to exploit the manufacturing potential. In stage 1, the production process is flexible and high cost, and becomes increasingly standardize, mechanized, and automated, resulting in an inflexible and cost-efficient process.