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What is the importance of the $\delta$ parameter for LLL bases called Lovász condition? Wiki seems to mention that the higher the $\delta$ the better. What does it mean when $\delta = 1$? Can we finding find shortest vector and closest vector easily if $\delta =1$? What are typical error in approximations when $\delta = 1$? In the SVP and CVP algorithm presented below, is there any connection to $\delta$ on the approximation error(wiki seems to mention that the higher the $\delta$ the better). http://www.cs.tau.ac.il/~odedr/teaching/lattices_fall_2004/ln/cvp.pdf Can $\delta \ge 1$ be satisfied and is there a consequence on the bases and estimate of minimum distance between lattice points and CVP distance estimates if this is so? Finally is there a reference that talks about connections to CVP and minimum distance? |
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What is the importance of the $\delta$ parameter for LLL bases called Lovász condition? What does it mean when $\delta = 1$? Can we finding shortest vector and closest vector easily if $\delta =1$? In the CVP algorithm presented below, is there any connection to $\delta$ on the approximation error (wiki seems to mention that the higher the $\delta$ the better). http://www.cs.tau.ac.il/~odedr/teaching/lattices_fall_2004/ln/cvp.pdf Can $\delta \ge 1$ be satisfied and is there a consequence on the bases and estimate of minimum distance between lattice points and CVP distance estimates if this is so? Finally is there a reference that talks about connections to CVP and minimum distance? |
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