Search results
Results from the WOW.Com Content Network
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.
GNNs are used as fundamental building blocks for several combinatorial optimization algorithms. [44] Examples include computing shortest paths or Eulerian circuits for a given graph, [ 39 ] deriving chip placements superior or competitive to handcrafted human solutions, [ 45 ] and improving expert-designed branching rules in branch and bound .
The multidimensional assignment problem (MAP) is a fundamental combinatorial optimization problem which was introduced by William Pierskalla. [1] This problem can be seen as a generalization of the linear assignment problem. [2] In words, the problem can be described as follows:
Classic combinatorial search problems include solving the eight queens puzzle or evaluating moves in games with a large game tree, such as reversi or chess. A study of computational complexity theory helps to motivate combinatorial search. Combinatorial search algorithms are typically concerned with problems that are NP-hard. Such problems are ...
Quadratic unconstrained binary optimization (QUBO), also known as unconstrained binary quadratic programming (UBQP), is a combinatorial optimization problem with a wide range of applications from finance and economics to machine learning. [1]
Ant colony optimization is based on the ideas of ant foraging by pheromone communication to form paths. Primarily suited for combinatorial optimization and graph problems. Particle swarm optimization is based on the ideas of animal flocking behaviour. Also primarily suited for numerical optimization problems.
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.
Each one of these areas is constituted by several sub-topics like Evolutionary Computation, Density Estimation, Spectral method, Combinatorial Optimization, Reinforcement learning, Meta learning, Convex optimization in the case of Theory and methods and Natural language processing, robotics, Signal processing, Information retrieval, perception ...