Sentence examples for I c for from inspiring English sources

In the following paragraphs, we define the mapping functions f i (c) for the three context attributes audience size, domain, and creator and publisher.

These steps show the calculations performed by H e u r i s t i c for a given set of LSP requests Q.

Then, i C is a proper lower semicontinuous convex function on H. So, we can define the resolvent J λ of ∂ i C for λ > 0, i.e., J λ x = ( I + λ ∂ i C ) − 1 x. for all x ∈ H.

Pixels are put into k bins where h i t is the number of pixels that falls into the i th bin for the target t, and h i c for the candidate c.

So, we can define the resolvent J λ ∂ i C of ∂ i C for λ > 0, i.e., J λ ∂ i C x = ( I + λ ∂ i C ) − 1 x. for all x ∈ H.

The H e u r i s t i c for Online-R-LSP randomly selects one of the ({p^{k}_{q}}) paths in the set P q for the current request q.

Then there exist constants α i, β i ∈ C for i = 1, …, m, such that v ( x n ) = ∑ j = 1 m α j v j ( x n ) and w ( x n ) = − ∑ j = 1 m β j w j ( x n ).

Then d ( x, x i ) ≺ c i for i = 1, 2. From (S3), we get c i − d ( x, x i ) ≻ 0 for i = 1, 2. It follows from (S12) that there exists a vector c ∈ Y with c ≻ 0 such that c ≺ c i − d ( x, x i ) for i = 1, 2. By (S3), we obtain d ( x, x i ) ≺ c i − c for i = 1, 2. Now using the triangle inequality and (S10), it easy to show that U ( x, c ) ⊂ U ( x 1, c 1 ) ∩ U ( x 2, c 2 ).

For simplicity, in our simulations all censoring times are taken as constant, i.e. C i = C for all i.

Suppose that there exists a positive integer j such that u j ≥ c, but u i < c for i < j.

From the definition, C 1 = ⋂ i = 1 ∞ C 1, i = C for all i ≥ 1 is closed and convex.