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  2. Sequential quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Sequential_quadratic...

    Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization which may be considered a quasi-Newton method.SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable, but not necessarily convex.

  3. Sequential linear-quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Sequential_linear...

    In the EQP phase of SLQP, the search direction of the step is obtained by solving the following equality-constrained quadratic program: + + (,,).. + = + =Note that the term () in the objective functions above may be left out for the minimization problems, since it is constant.

  4. Gekko (optimization software) - Wikipedia

    en.wikipedia.org/wiki/Gekko_(optimization_software)

    GEKKO works on all platforms and with Python 2.7 and 3+. By default, the problem is sent to a public server where the solution is computed and returned to Python. There are Windows, MacOS, Linux, and ARM (Raspberry Pi) processor options to solve without an Internet connection.

  5. Quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Quadratic_programming

    Quadratic programming (QP) is the process of solving certain mathematical optimization problems involving quadratic functions.Specifically, one seeks to optimize (minimize or maximize) a multivariate quadratic function subject to linear constraints on the variables.

  6. Quadratically constrained quadratic program - Wikipedia

    en.wikipedia.org/wiki/Quadratically_constrained...

    A solver for large scale optimization with API for several languages (C++, java, .net, Matlab and python) TOMLAB: Supports global optimization, integer programming, all types of least squares, linear, quadratic and unconstrained programming for MATLAB. TOMLAB supports solvers like CPLEX, SNOPT and KNITRO. Wolfram Mathematica

  7. Nelder–Mead method - Wikipedia

    en.wikipedia.org/wiki/Nelder–Mead_method

    Nelder-Mead optimization in Python in the SciPy library. nelder-mead - A Python implementation of the Nelder–Mead method; NelderMead() - A Go/Golang implementation; SOVA 1.0 (freeware) - Simplex Optimization for Various Applications - HillStormer, a practical tool for nonlinear, multivariate and linear constrained Simplex Optimization by ...

  8. Limited-memory BFGS - Wikipedia

    en.wikipedia.org/wiki/Limited-memory_BFGS

    Since BFGS (and hence L-BFGS) is designed to minimize smooth functions without constraints, the L-BFGS algorithm must be modified to handle functions that include non-differentiable components or constraints. A popular class of modifications are called active-set methods, based on the concept of the active set. The idea is that when restricted ...

  9. Python for S60 - Wikipedia

    en.wikipedia.org/wiki/Python_for_S60

    Python for S60, also called PyS60—a term reminiscent of the Unix naming convention—is a port of the Python programming language developed by Nokia for its S60 software platform, originally based on Python 2.2.2 from 2002. [1] The final version, PyS60-2.0.0, was released on 11 February 2010.

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