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We propose a new approach to LAN topological design named Productivity-aware reliable network design (PA-RND) that takes into account the productivity associated to each node of the network, minimizing not only the CAPEX but also the expected cost attributable to network downtimes over a certain period of network operation.
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They minimize not only theL∞-norm of the corresponding matrix function but also the essential suprema of all further singular values.
Americans tend to minimize not only the secular convictions of the founders, but also the secularist contribution to later social reform movements.
Then, we present an improved thermal-aware floorplanner based on a new formulation of the cost function that minimizes not only peak temperature, but also thermal gradients.
Water integration is achieved by exploiting all possibilities of water reuse/recycle to minimize not only freshwater consumption, but also wastewater generation.
The word superoptimal means that we are seeking F ∈ H ∞ (matrix-valued) to minimize not only sup {|| Φ − F || : |ζ| = 1} = sup{s0 − F : |ζ| = 1} but also the suprema of further singular values sj − F, j ≥ 1.
The control strategies to achieve precise servo system are summarized in two main points: an acceleration control is adopted in an inner loop to ensure robustness of speed of each axis against disturbances, and then a maximum error comparison method has been introduced to minimize not only position synchronous errors but also speed errors of all motor axes.
This model minimizes not only the manufacturing and remanufacturing costs, the setup cost and the inventory holding and backlogging costs over the planning horizon, but also the energy costs paid for the utilization of machines and the compression of processing times.
The objective of the model is to minimize not only the travel distance and number of available vehicles along with the capacity and aggregated route duration restrictions but also the amount of fuel consumptions and green house emissions along with their total costs.
The goal of RLS methods is to minimize not only the last error but also the sum of all past output errors.
For example, if we focus on the squared error between the estimated and true parameters, our methods attempts to minimize not only the variance of the estimates, as in standard A-optimal designs [ 30, 31], but also a term related to the bias of the estimate.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com