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  2. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    Modeling tools are separate pieces of software that let the user specify an optimization in higher-level syntax. They manage all transformations to and from the user's high-level model and the solver's input/output format. The table below shows a mix of modeling tools (such as CVXPY and Convex.jl) and solvers (such as CVXOPT and MOSEK).

  3. Convex function - Wikipedia

    en.wikipedia.org/wiki/Convex_function

    This characterization of convexity is quite useful to prove the following results. A convex function f {\displaystyle f} of one real variable defined on some open interval C {\displaystyle C} is continuous on C . {\displaystyle C.} f {\displaystyle f} admits left and right derivatives , and these are monotonically non-decreasing .

  4. Subgradient method - Wikipedia

    en.wikipedia.org/wiki/Subgradient_method

    Let : be a convex function with domain . A classical subgradient method iterates (+) = () where () denotes any subgradient of at (), and () is the iterate of . If is differentiable, then its only subgradient is the gradient vector itself.

  5. List of convexity topics - Wikipedia

    en.wikipedia.org/wiki/List_of_convexity_topics

    Convexity (finance) - refers to non-linearities in a financial model. When the price of an underlying variable changes, the price of an output does not change linearly, but depends on the higher-order derivatives of the modeling function. Geometrically, the model is no longer flat but curved, and the degree of curvature is called the convexity.

  6. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    If the objective function is concave (maximization problem), or convex (minimization problem) and the constraint set is convex, then the program is called convex and general methods from convex optimization can be used in most cases. If the objective function is quadratic and the constraints are linear, quadratic programming techniques are used.

  7. How Some of California's Worst Schools Got Better at Teaching ...

    www.aol.com/news/californias-worst-schools-got...

    This led to a legal settlement in which the state's 75 worst-performing elementary schools agreed to invest in evidence-based reading instruction—that is, in training teachers to use techniques ...

  8. Georgia Milestones: Best and worst school districts in ...

    www.aol.com/georgia-milestones-best-worst-school...

    Here are the school districts with the highest percentage of third-grade students reading at or above the grade level: Oconee County School District: 88.5% Bremen City School District: 88.4%

  9. Georgia Milestones: Best and worst Augusta-area schools in ...

    www.aol.com/georgia-milestones-best-worst...

    Here are the schools that ranked in the higher 95th percentile in amount of students reading at or above their grade level: C.T. Walker Magnet School (Richmond County) Riverside Elementary ...