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  2. Loss function - Wikipedia

    en.wikipedia.org/wiki/Loss_function

    In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture. [4]

  3. Ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Ordinary_least_squares

    In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...

  4. Loss functions for classification - Wikipedia

    en.wikipedia.org/wiki/Loss_functions_for...

    In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). [1]

  5. Without loss of generality - Wikipedia

    en.wikipedia.org/wiki/Without_loss_of_generality

    In many scenarios, the use of "without loss of generality" is made possible by the presence of symmetry. [2] For example, if some property P(x,y) of real numbers is known to be symmetric in x and y, namely that P(x,y) is equivalent to P(y,x), then in proving that P(x,y) holds for every x and y, one may assume "without loss of generality" that x ...

  6. Business mathematics - Wikipedia

    en.wikipedia.org/wiki/Business_mathematics

    Business mathematics comprises mathematics credits taken at an undergraduate level by business students. The course [3] is often organized around the various business sub-disciplines, including the above applications, and usually includes a separate module on interest calculations; the mathematics itself comprises mainly algebraic techniques. [1]

  7. Mathematical economics - Wikipedia

    en.wikipedia.org/wiki/Mathematical_economics

    Mathematical economics is the application of mathematical methods to represent theories and analyze problems in economics.Often, these applied methods are beyond simple geometry, and may include differential and integral calculus, difference and differential equations, matrix algebra, mathematical programming, or other computational methods.

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    mail.aol.com

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  9. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    The optimization of portfolios is an example of multi-objective optimization in economics. Since the 1970s, economists have modeled dynamic decisions over time using control theory. [14] For example, dynamic search models are used to study labor-market behavior. [15] A crucial distinction is between deterministic and stochastic models. [16]