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The difficulties include long simulation times, large computer memory requirements, and computational instability.
Distributing the storage and computations on several machines benefits both memory requirements and computational durations.
The developed S-DDM scheme reduces computational complexities, large memory requirements and computational costs.
Moreover, storage requirements and computational cost compare competitively with mesh refinement schemes based on hierarchical trees.
As a decoding method, the Fano algorithm is selected for its relatively low memory requirements and computational complexity.
The reconstruction performance is optimized while minimizing the number of irradiation directions (experimental hardware requirements) and computational effort.
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To reduce the storage requirement and computational complexity of RRDA for high dimensional, low sample size data, we also establish an equivalent reduced model of RRDA.
The vegetation model chosen is more general and yet simple in formulation making to a top-down analysis of a system possible thereby reducing the data requirement and computational complexity.
Discrete differential dynamic programming (DDDP) is a classical method to alleviate the dimensionality problem of dynamic programming for the OOHS, but its memory requirement and computational time still grows exponentially with the increasing number of plants.
The huge public orthogonal matrix results in huge storage space requirement and computational load.
It is better than other methods in terms of memory requirement and computational speed with good convergence.
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