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  2. Automated planning and scheduling - Wikipedia

    en.wikipedia.org/wiki/Automated_planning_and...

    Given a description of the possible initial states of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem is to synthesize a plan that is guaranteed (when applied to any of the initial states) to generate a state which contains the desired goals (such a state is called a goal state).

  3. Group method of data handling - Wikipedia

    en.wikipedia.org/wiki/Group_method_of_data_handling

    GEvom — Free upon request for academic use. Windows-only. GMDH Shell — GMDH-based, predictive analytics and time series forecasting software. Free Academic Licensing and Free Trial version available. Windows-only. KnowledgeMiner — Commercial product. Mac OS X-only. Free Demo version available. PNN Discovery client — Commercial product ...

  4. Combinatorial optimization - Wikipedia

    en.wikipedia.org/wiki/Combinatorial_optimization

    A minimum spanning tree of a weighted planar graph.Finding a minimum spanning tree is a common problem involving combinatorial optimization. Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, [1] where the set of feasible solutions is discrete or can be reduced to a discrete set.

  5. Knapsack problem - Wikipedia

    en.wikipedia.org/wiki/Knapsack_problem

    The knapsack problem is the following problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine which items to include in the collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.

  6. Branch and bound - Wikipedia

    en.wikipedia.org/wiki/Branch_and_bound

    Branch and bound (BB, B&B, or BnB) is a method for solving optimization problems by breaking them down into smaller sub-problems and using a bounding function to eliminate sub-problems that cannot contain the optimal solution. It is an algorithm design paradigm for discrete and combinatorial optimization problems, as well as mathematical ...

  7. Quadratic unconstrained binary optimization - Wikipedia

    en.wikipedia.org/wiki/Quadratic_unconstrained...

    [2] [3] Embeddings for machine learning models include support-vector machines, clustering and probabilistic graphical models. [4] Moreover, due to its close connection to Ising models , QUBO constitutes a central problem class for adiabatic quantum computation , where it is solved through a physical process called quantum annealing .

  8. File:Deep Learning Applied in Computer Vision.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Deep_Learning_Applied...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  9. Evolutionary multimodal optimization - Wikipedia

    en.wikipedia.org/wiki/Evolutionary_multimodal...

    In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple (at least locally optimal) solutions of a problem, as opposed to a single best solution. Evolutionary multimodal optimization is a branch of evolutionary computation, which is closely related to machine learning.