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To address such issue, we introduce a Software Defined Networking (SDN) service that breaks the problem of network configuration into tractable pieces and involves virtual bridges instead of references to static endpoints.
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While these approaches characterize ways to break the problem of price-induced herding of loads, they do not characterize appropriate incentive structures that induce truthful behavior of system participants.
To explain observed variation in costs of resistance, we suggest breaking the problem of identifying costs in two parts.
This essentially breaks the problem into a sequence of convex optimization problems for which the solution can be readily computed.
Blue Matter computes these forces using the Particle Particle Particle Mesh Ewald (P3ME) method which breaks the problem up into two pieces, one of which requires the use of three-dimensional Fast Fourier Transforms with global data dependencies and the other which involves computing interactions between pairs of particles within a cut-off distance.
To implement parallel computing, one first needs to break the problem into discrete "chunks" of work, so that they can be distributed to run on multiple processors.
The disadvantage of breaking the problem into smaller blocks is that the boundaries of each block are essentially wasted (although a minimal amount of reuse can occur) because they must be resent when the adjacent block is calculated.
Calculating the probability of multiple events is a matter of breaking the problem down into separate probabilities.
For some computing problems in banking, science, engineering and computer animation, it is possible to break the problem up into thousands or hundreds of thousands of smaller tasks.
Aryabhata's general solution for linear indeterminate equations, which Bhaskara I called kuttakara ("pulverizer"), consisted of breaking the problem down into new problems with successively smaller coefficients essentially the Euclidean algorithm and related to the method of continued fractions.
The basic idea is again to break the problem up into the family of autonomous linear problems and "feedback" via the Euler algorithm, exactly as for solutions with values in (L^p({{mathbb R}^{N}})) (in Sect. 4, 5).
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