Below we formalize the problem.
In Section 1.2, we formalize the problem of maximal profit.
We formalize the problem and represent its mathematical form.
To formalize the problem, we first define a function ({mathsf {diff}} to measure the difference of (mathsf {GPARs}).
We formalize the problem of active data warehouse, and we introduce a new architecture for active data warehouse establishment; 2.
We formalize the problem by defining the notion of a constrained dataset and a corresponding constrained likelihood that we seek to optimize.
We formalize the problem of maximizing network spectral efficiency optimizing (a) QAM size, (b) power allocation, (c) time allocation, and (d) relays transmitting and receiving at each phase.
The model is used to formalize the problem of optimal distribution of computational load to an arbitrary set of resources across a heterogeneous cluster.
We formalize the problem as global optimization with non-linear objectives and linear constraints with the twofold objective of maximizing user throughput and minimizing overall network cost.
We formalize the problem as follows: At each step, we aim to compute the most probable next location given the current location, as well as other context variables, such as time of the day or call activity.
In the next section,we present the data model used to represent a multi-modal transporation system of a city, and formalize the problem of efficient computation of the coverage using random walkers.
