In addition to finding goals, local search algorithms are useful for solving pure (), in which the aim is to find the best state according to an objective function.除了寻找目标之外,局部搜索算法对解决纯()也很有效。其目的是根据一个目标函数找到其最好的状态。
Once you understand how an SBC can be used to solve one problem, you can adapt the SBC algorithm presented here to solve your own problems. As this article will demonstrate, SBC algorithms are best suited for solving complex combinatorial problems that hav...
Similarly, the algorithm is faster for small or relatively dense problems when you specify H as full. Trust-Region-Reflective Least Squares Trust-Region-Reflective Least Squares Algorithm Many of the methods used in Optimization Toolbox solvers are based on trust regions, a simple yet powerful ...
Pathfinding algorithms are useful for understanding the way that our data is connected. In this chapter we started out with the fundamental Breadth and Depth First algorithms, before moving onto Dijkstra and other shortest path algorithms. We also looked at variants of the shortest path algorithms ...
Due to the nonlinear multi-objective nature of these problems, the traditional methods are not suitable approaches for solving large-scale power system operation dilemmas. The integration of optimization algorithms into power systems has been discussed in several textbooks, but this is the first to ...
Ruhe, Rational Krylov algorithms for nonsymmetric eigenvalue problems, II: Matrix ... Axel,Ruhe - 《Bit Numerical Mathematics》 被引量: 406发表: 1994年 Arnoldi versus nonsymmetric Lanczos algorithms for solving nonsymmetric matrix eigenvalue problems We obtain several results which may be useful in ...
Metaheuristics are a family of algorithmic techniques that are useful for solving difficult problems. Roughly speaking, the difficulty or hardness of a problem is the quantity of computational...doi:10.1007/978-3-319-93073-2_1Chopard, BastienTomassini, Marco...
for other instances. Unsupervised learning is useful in cases where the challenge is to discover implicit relationships in a givenunlabeleddataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there is some form of feedback available for each predictive ...
When p=2, minimizing J(u) is equivalent to solving a single linear problem, which is clearly faster than solving hundreds of linear problems as required by a barrier method. As p gets further away from p=2, naive solvers work less well and proper convex optimization algorithms are required...
Three algorithms for reconstructing objects from their projections are compared. Such algorithms are useful in locating the position and shape of tumors fr... HSW Rowland - 《Computer Graphics & Image Processing》 被引量: 27发表: 1973年 The nearness problems for symmetric centrosymmetric with a spe...