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  2. Bogacki–Shampine method - Wikipedia

    en.wikipedia.org/wiki/Bogacki–Shampine_method

    The Bogacki–Shampine method is implemented in the ode3 for fixed step solver and ode23 for a variable step solver function in MATLAB (Shampine & Reichelt 1997). Low-order methods are more suitable than higher-order methods like the Dormand–Prince method of order five, if only a crude approximation to the solution is required. Bogacki and ...

  3. Quadratic unconstrained binary optimization - Wikipedia

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

    QUBO is an NP hard problem, and for many classical problems from theoretical computer science, like maximum cut, graph coloring and the partition problem, embeddings into QUBO have been formulated. [2] [3] Embeddings for machine learning models include support-vector machines, clustering and probabilistic graphical models. [4]

  4. Largest differencing method - Wikipedia

    en.wikipedia.org/wiki/Largest_differencing_method

    In computer science, the largest differencing method is an algorithm for solving the partition problem and the multiway number partitioning. It is also called the Karmarkar–Karp algorithm after its inventors, Narendra Karmarkar and Richard M. Karp . [ 1 ]

  5. Bin packing problem - Wikipedia

    en.wikipedia.org/wiki/Bin_packing_problem

    Furthermore, research is mostly interested in the optimization variant, which asks for the smallest possible value of . A solution is optimal if it has minimal K {\displaystyle K} . The K {\displaystyle K} -value for an optimal solution for a set of items I {\displaystyle I} is denoted by O P T ( I ) {\displaystyle \mathrm {OPT} (I)} or just O ...

  6. Limited-memory BFGS - Wikipedia

    en.wikipedia.org/wiki/Limited-memory_BFGS

    The algorithm starts with an initial estimate of the optimal value, , and proceeds iteratively to refine that estimate with a sequence of better estimates ,, ….The derivatives of the function := are used as a key driver of the algorithm to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of ().

  7. Pseudo-spectral method - Wikipedia

    en.wikipedia.org/wiki/Pseudo-spectral_method

    Pseudo-spectral methods, [1] also known as discrete variable representation (DVR) methods, are a class of numerical methods used in applied mathematics and scientific computing for the solution of partial differential equations.

  8. Broyden's method - Wikipedia

    en.wikipedia.org/wiki/Broyden's_method

    Newton's method for solving f(x) = 0 uses the Jacobian matrix, J, at every iteration. However, computing this Jacobian can be a difficult and expensive operation; for large problems such as those involving solving the Kohn–Sham equations in quantum mechanics the number of variables can be in the hundreds of thousands. The idea behind Broyden ...

  9. MUSCL scheme - Wikipedia

    en.wikipedia.org/wiki/MUSCL_scheme

    It is a Riemann-solver-free, second-order, high-resolution scheme that uses MUSCL reconstruction. It is a fully discrete method that is straight forward to implement and can be used on scalar and vector problems, and can be viewed as a Rusanov flux (also called the local Lax-Friedrichs flux) supplemented with high order reconstructions.