Greedy approach

The algorithm checked every investigated reaction for a minimum number of needed reactions and reactants needed to produce the products. A greedy approach was employed for minimizing the number of reactants and reactions needed to produce the products. 

Here, we will actually embrace the seemingly unreasonable bi- or multifurcated lone pairs, because in our view, this will help our later determination of molecular geometry. In particular, we will take a greedy approach to first mandate the d electrons to reside on particular orbitals as lone pairs and then use the face-dual argument to rearrange all the bonding directions for the specified number of ligands. This approach is listed as follows ... 

We note that, at its simplest, the optimization will be on an epoch by epoch basis (the so-called greedy or myopic approach). In this case, the mode is chosen just to optimize for the next epoch and defer consideration of future behavior. A more sophisticated system would look several epochs ahead in applying the measure of effectiveness, though it would also update the scheduling policy on an epoch by epoch basis. Such an approach is, a priori, very computer intensive, and much work is needed to develop shortcuts to calculation of the optimal policy. Sometimes it may be appropriate to choose to measure the effectiveness of a policy only at the last epoch of application of that policy. 

The second approach leaves the data fixed, and introduces a random element into the selection of the splitting variable. So if, for example, predictors x29, x47, and x82 were the only significant splitters and had multiplicity-adjusted p values of say 2 x 10-7, 5 x 10-8, and 3 x 10-3, the conventional greedy algorithm would pick x47 as the splitting variable as it was the most significant. The RRP procedure would pick one of these three at random. Repeating the analysis with fresh random choices would then lead to a forest of trees different random choices will create different trees. 

Disappointingly, we found that a 2-opt optimization of the greedy TSP-tour leads to only 0.3% improvement (Table 1). We tried a few approaches to further improve the TSP solution (or to verify its proximity to the optimal solution), in particular, solving minimum length cycle cover problem with further merging of cycles in the cover into a hamiltonian path. A cycle cover is a set of (simple) cycles such that each vertex in the graph is contained in exactly one cycle. The... 

The alternative to the first-come, first-served approach is to impose limits of some kind. The argument could be made that we do not want the early industrial developer who is greedy and uses up all of the quota because we may feel that Industrial development in the community is better served by using up the quota over a period of time. This objective can be met if an annual limit is imposed on the additional pollution to be allowed under the quota in any one year. An annual limit will close out some of the larger, earlier developers who otherwise would use up a substantial share of the quota. Another alternative is to limit the increment in pollution allowable to any one industrial developer. That allocation method has the same effect as an annual quota. 

Ant Colony Optimization (ACO) meta-heuristics is one such technique that is based on the cooperative forging strategy of real ants [1],[2]. In this approach, several artificial ants perform a sequence of operations iteratively. Ants are guided by a greedy heuristic algorithm which is problem dependenf that aid their search for better solutions iteratively. Ants seek solutions using information gathered previously to... 

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