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  2. Feasible region - Wikipedia

    en.wikipedia.org/wiki/Feasible_region

    For example, if the feasible region is defined by the constraint set {x ≥ 0, y ≥ 0}, then the problem of maximizing x + y has no optimum since any candidate solution can be improved upon by increasing x or y; yet if the problem is to minimize x + y, then there is an optimum (specifically at (x, y) = (0, 0)).

  3. MCS algorithm - Wikipedia

    en.wikipedia.org/wiki/MCS_algorithm

    For mathematical optimization, Multilevel Coordinate Search (MCS) is an efficient [1] algorithm for bound constrained global optimization using function values only. [2] To do so, the n-dimensional search space is represented by a set of non-intersecting hypercubes (boxes). The boxes are then iteratively split along an axis plane according to ...

  4. Gekko (optimization software) - Wikipedia

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

    GEKKO is an extension of the APMonitor Optimization Suite but has integrated the modeling and solution visualization directly within Python. A mathematical model is expressed in terms of variables and equations such as the Hock & Schittkowski Benchmark Problem #71 [ 2 ] used to test the performance of nonlinear programming solvers.

  5. Comparison of optimization software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_optimization...

    The optimization software will deliver input values in A, the software module realizing f will deliver the computed value f(x). In this manner, a clear separation of concerns is obtained: different optimization software modules can be easily tested on the same function f, or a given optimization software can be used for different functions f.

  6. Semidefinite programming - Wikipedia

    en.wikipedia.org/wiki/Semidefinite_programming

    Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize) over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron.

  7. Test functions for optimization - Wikipedia

    en.wikipedia.org/.../Test_functions_for_optimization

    Here some test functions are presented with the aim of giving an idea about the different situations that optimization algorithms have to face when coping with these kinds of problems. In the first part, some objective functions for single-objective optimization cases are presented.

  8. Second-order cone programming - Wikipedia

    en.wikipedia.org/wiki/Second-order_cone_programming

    is the optimization variable. ‖ x ‖ 2 {\\displaystyle \\lVert x\\rVert _{2}} is the Euclidean norm and T {\\displaystyle ^{T}} indicates transpose . [ 1 ] The "second-order cone" in SOCP arises from the constraints, which are equivalent to requiring the affine function ( A x + b , c T x + d ) {\\displaystyle (Ax+b,c^{T}x+d)} to lie in the ...

  9. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, R, JavaScript, Fortran, and C#. It has no external dependencies. A convenient thin wrapper to Python is available via the highspy PyPI package. Although generally single-threaded, some solver components can utilize multi-core ...