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  2. AGESA - Wikipedia

    en.wikipedia.org/wiki/AGESA

    Support for Ryzen 7000X3D March 2023 1.0.0.4 Support for Ryzen 7000 with 65 Watt January 2023 1.0.0.3 Patch A Improved GPU compatibility for GeForce RTX 40 series, Optimize for AMD Ryzen Master Utility September 2022 1.0.0.3 Optimized system settings 1.0.0.2 Optimized system stability 1.0.0.1 Patch H Improved RAM-compatibility

  3. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    Optimization problems are often multi-modal; that is, they possess multiple good solutions. They could all be globally good (same cost function value) or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer.

  4. Discrete optimization - Wikipedia

    en.wikipedia.org/wiki/Discrete_optimization

    Three notable branches of discrete optimization are: [2] combinatorial optimization, which refers to problems on graphs, matroids and other discrete structures; integer programming

  5. Multi-objective optimization - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_optimization

    Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.

  6. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints. [ 4 ] [ 5 ] Curve fitting can involve either interpolation , [ 6 ] [ 7 ] where an exact fit to the data is required, or smoothing , [ 8 ] [ 9 ] in which a "smooth ...

  7. Powell's method - Wikipedia

    en.wikipedia.org/wiki/Powell's_method

    Powell's method, strictly Powell's conjugate direction method, is an algorithm proposed by Michael J. D. Powell for finding a local minimum of a function. The function need not be differentiable, and no derivatives are taken.

  8. Trajectory optimization - Wikipedia

    en.wikipedia.org/wiki/Trajectory_optimization

    Depending on the configuration, open-chain robotic manipulators require a degree of trajectory optimization. For instance, a robotic arm with 7 joints and 7 links (7-DOF) is a redundant system where one cartesian position of an end-effector can correspond to an infinite number of joint angle positions, thus this redundancy can be used to optimize a trajectory to, for example, avoid any ...

  9. Column generation - Wikipedia

    en.wikipedia.org/wiki/Column_generation

    The master problem is the original problem with only a subset of variables being considered. The subproblem is a new problem created to identify an improving variable (i.e. which can improve the objective function of the master problem). The algorithm then proceeds as follow: Initialise the master problem and the subproblem; Solve the master ...

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