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  2. SU2 code - Wikipedia

    en.wikipedia.org/wiki/SU2_code

    Fix update of dual-time solver for species transport. by @bigfooted in #2260; Fix bug in inlet profile writer. by @bigfooted in #2267; Update to start volume averaging after StartWindowIteration. by @ShiheJia in #2252; Fix SU2_DOT without DV vars by @pcarruscag in #2270; Fix CHT boundary problem for flamelet computations by @Cristopher-Morales ...

  3. 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 ...

  4. Min-conflicts algorithm - Wikipedia

    en.wikipedia.org/wiki/Min-conflicts_algorithm

    The randomness helps min-conflicts avoid local minima created by the greedy algorithm's initial assignment. In fact, Constraint Satisfaction Problems that respond best to a min-conflicts solution do well where a greedy algorithm almost solves the problem. Map coloring problems do poorly with Greedy Algorithm as well as Min-Conflicts. Sub areas ...

  5. Spectral concentration problem - Wikipedia

    en.wikipedia.org/wiki/Spectral_concentration_problem

    The three leading Slepian sequences for T=1000 and 2WT=6. Note that each higher order sequence has an extra zero crossing. The spectral concentration problem in Fourier analysis refers to finding a time sequence of a given length whose discrete Fourier transform is maximally localized on a given frequency interval, as measured by the spectral concentration.

  6. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    Many optimization problems can be equivalently formulated in this standard form. For example, the problem of maximizing a concave function can be re-formulated equivalently as the problem of minimizing the convex function . The problem of maximizing a concave function over a convex set is commonly called a convex optimization problem.

  7. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [33] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...

  8. Ant colony optimization algorithms - Wikipedia

    en.wikipedia.org/wiki/Ant_colony_optimization...

    To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph. In the first step of each iteration, each ant stochastically constructs a solution, i.e. the order in which the edges in the graph should be followed.

  9. Metric k-center - Wikipedia

    en.wikipedia.org/wiki/Metric_k-center

    In graph theory, the metric k-center problem or vertex k-center problem is a classical combinatorial optimization problem studied in theoretical computer science that is NP-hard. Given n cities with specified distances, one wants to build k warehouses in different cities and minimize the maximum distance of a city to a warehouse.