Abstract In 1992, Kalai and Kleitman proved that the diameter of a d -dimensional polyhedron with n facets is at most n 2 + log 2 d . In 2014, Todd improved the Kalai–Kleitman bound to ( n − d ) log 2 d . We improve the Todd bound to ( n − d ) − 1 + log 2 d for n ≥ d ≥ 7 , ( n − d ) − 2 + log 2 d for n ≥ d ≥ 37 , and ( n − d ) − 3 + log 2 d + O 1 ∕ d for n ≥ d ≥ 1 .
We also note that there are studies that attempt to understand the behavior of @math when the number of facets is sufficiently large. Gallagher and Kim @cite_23 provided an upper bound on the diameter of a normal simplicial complex and showed the tail-polynomiality ; more specifically, they showed that the diameter is bounded from above by a polynomial in @math when @math is sufficiently large. An alternative simpler proof for such tail-polynomial upper bounds can be found in Mizuno and Sukegawa @cite_13 . In contrast, in this paper, we assume that @math is large, and try to utilize this assumption to strengthen the previous results.
@cite_13 @cite_23
2298099604 2338231399 30524954 1990538552
In 1992, Kalai and Kleitman proved a quasipolynomial upper bound on the diameters of convex polyhedra. Todd and Sukegawa-Kitahara proved tail-quasipolynomial bounds on the diameters of polyhedra. These tail bounds apply when the number of facets is greater than a certain function of the dimension. We prove tail-quasipolynomial bounds on the diameters of polytopes and normal simplicial complexes. We also prove tail-polynomial upper bounds on the diameters of polyhedra. Let @math denote the maximum diameter of a @math -dimensional polyhedron with @math facets. In this paper, we propose a unified analysis of a recursive inequality about @math established by Kalai and Kleitman in 1992. This yields much simpler proofs of a tail--polynomial and tail--almost--linear bounds on @math which are recently discussed by Gallagher and Kim. In this paper, we prove tight lower bounds on the smallest degree of a nonzero polynomial in the ideal generated by @math or @math in the polynomial ring @math , @math are coprime, which is called over @math . The immunity of @math is lower bounded by @math , which is achievable when @math is a multiple of @math ; the immunity of @math is exactly @math for every @math and @math . Our result improves the previous bound @math by Green. We observe how immunity over @math is related to @math circuit lower bound. For example, if the immunity of @math over @math is lower bounded by @math , and @math , then @math requires @math circuit of exponential size to compute. We consider the problem of approximating a Boolean functionf∶ 0,1 n → 0,1 by the sign of an integer polynomialp of degreek. For us, a polynomialp(x) predicts the value off(x) if, wheneverp(x)≥0,f(x)=1, and wheneverp(x)<0,f(x)=0. A low-degree polynomialp is a good approximator forf if it predictsf at almost all points. Given a positive integerk, and a Boolean functionf, we ask, “how good is the best degreek approximation tof?” We introduce a new lower bound technique which applies to any Boolean function. We show that the lower bound technique yields tight bounds in the casef is parity. Minsky and Papert [10] proved that a perceptron cannot compute parity; our bounds indicate exactly how well a perceptron canapproximate it. As a consequence, we are able to give the first correct proof that, for a random oracleA, PP A is properly contained in PSPACE A . We are also able to prove the old AC0 exponential-size lower bounds in a new way. This allows us to prove the new result that an AC0 circuit with one majority gate cannot approximate parity. Our proof depends only on basic properties of integer polynomials.