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Exact(5)
We drop the dependence on T of the estimated matrices and vectors.
For simplicity, we drop the dependence in t: F ( x ) = x 1 + α sin ( log ( 1 + x ) ).
In the following, we focus on a specific set of active MSs and, furthermore, we drop the dependence on the block index for simplicity of notation.
fWe shall henceforth, without loss of generality, drop the dependence of π on the signals St. gWe assume c < p 3 S 2, ~ S 1, π 3 with probability 1. hWe assume that this return is based on a known success probability (no learning needs to take place on the part of the VC about the parameters).
For conciseness, in the sequel we shall drop the dependence of v on (x, u, e) from the notation.
Similar(55)
Hence, one often drops the dependence on the initial condition in (A5).
In order to achieve rate (21), we consider the specific distributions (dropping the dependence on the time index with a slight abuse of notation) and, with independent of.
In order to achieve rate (8), we further specialize the distributions as and as (dropping the dependence on the time index with a slight abuse of notation) (B.2). where, independent of all and " " denotes convolution.
In order to prove the existence of extremal elements of the solution set, we drop the -dependence of the operator.
(10) Note that we drop the λ-dependence when it is clear from the context.
For example, for simplicity, we drop the x-dependence j_{1} u)= begin{cases} frac{3}{16}cvert uvert ^{frac{16}{3}} & mbox{if } vert uvert geq 1, frac{3}{16}cvert uvert ^{2} & mbox{if } vert uvert < 1, end{cases} where (0< cleqfrac{8}{3}lambda_{1} a).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com