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The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [1] Haupt et al. [2] and from Rody Oldenhuis software. [3] Given the number of problems (55 in total), just a few are presented here. The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and ...
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 ...
Equivalence problem for star-free regular expressions with squaring. [21] Covering for linear grammars [37] Structural equivalence for linear grammars [38] Equivalence problem for Regular grammars [39] Emptiness problem for ET0L grammars [40] Word problem for ET0L grammars [41] Tree transducer language membership problem for top down finite ...
Reconfiguration problems concern the connectivity of a state space of solutions to a combinatorial problem. For instance, testing whether two 4-colorings of a graph can be connected to each other by moves that change the color of one vertex at a time, maintaining at each step a valid 4-coloring, is PSPACE-complete, [ 8 ] even though the same ...
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.
The switch parser function, coded as "#switch", selects the first matching branch in a list of choices, acting as a case statement. Each branch can be a value , an expression ( calculation ), or a template call, [ 1 ] evaluated and compared to match the value of the switch.
In mathematical optimization, the Rosenbrock function is a non-convex function, introduced by Howard H. Rosenbrock in 1960, which is used as a performance test problem for optimization algorithms. [1] It is also known as Rosenbrock's valley or Rosenbrock's banana function. The global minimum is inside a long, narrow, parabolic-shaped flat ...
If items can share space in arbitrary ways, the bin packing problem is hard to even approximate. However, if space sharing fits into a hierarchy, as is the case with memory sharing in virtual machines, the bin packing problem can be efficiently approximated. Another variant of bin packing of interest in practice is the so-called online bin ...