Greedy Algorithm贪心算法
10_Greedy
We have had to design judicious stopping criteria and employ efficient solvers for the three main sub-problems of the algorithm, including an efficient GPU implementation that alleviates the main bottleneck for large datasets. The performance of the method is evaluated on three examples: an ...
Figure 1.Image Clustering (left), Facility Location (middle), Parkison Telemonitoring (right). For each example we computed both the sharpness parameters and optimal solutions to compare predicted vs actual performance. In all three examples sharpness explains a significant portion of the greedy algo...
We discuss also some important examples. It is already known (see DeVore and Temlyakov,Adv. Comput. Math.5(1996), 173–187) that the Pure Greedy Algorithm for some dictionaries has a saturation property. We construct an example which shows that a natural generalization of the Pure Greedy ...
We also analyze the performance of the greedy algorithm when X is an independence system instead of a matroid. Then we derive two bounds, both tight: The first one is [1−(1−α/K)k]/α where K and k are the sizes of the largest and smallest maximal independent sets in X ...
there is an efficient algorithm for finding an optimal solution) just from knowing that some data is measured on an ordinal scale of measurement. 2 Formulation of the Problem Throughout x will denote a vector (x 1 , . . . , x n ) T , and R n + will denote a set of all ...
behavioral modelingIn this letter, a simplified sparse parameter identification algorithm is proposed to estimate the coefficients of the power amplifier (PA) behavioral model. The main idea is to select the kernel one by one from the complete model, where the criteria of selection are according ...
For epsilon greedy, you will most likely usePointdistributions, since the algorithm only cares about the mean of the reward estimate. Other distributions can be used, as long as they implement aMean()that returns well-defined values. For Thompson sampling, it is recommended to useNormalorBetadi...
tonetal.recentlyintroducedagreedylayer-wiseunsupervisedlearningalgorithm forDeepBeliefNetworks(DBN),agenerativemodelwithmanylayersofhidden causalvariables.Inthecontextoftheaboveoptimizationproblem,westudythisal- gorithmempiricallyandexplorevariantstobetterunderstanditssuccessandextend ittocaseswheretheinputsarecontinuousor...