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Exact(11)
Hence, This implies that is convex.
Hence, This implies that solves (4.25) for and any.
Hence This implies that converges weakly to This completes the proof of Theorem 3.4.
Hence, this implies important factor allocation distortions that jeopardize total factor productivity and overall competitiveness.
Hence, this implies that the proposed scheme be suitable for low signal-to-noise-ratio (SNR) case.
Since is an -strongly -accretive mapping (and hence, ), this implies from (3.35) that the sequence converges strongly to for (3.36).
Similar(49)
Hence So this implies that (4.19).
Hence, we have This implies that (3.13).
It follows from Lemma 2.6 that is a constant sequence, and hence for all This implies that, that is,.
Hence (d z,Tz)=d Tz,z =0), this implies that (Tz=z), that is, z is a fixed point of T. Finally, to prove the uniqueness of a fixed point, let (z^in X) be another fixed point of T such that (Tz^=z^).
Hence this inequality implies the uniform boundedness (3.1) for the convolution part.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com