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Electronic Colloquium on Computational Complexity

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Reports tagged with Knapsack:
TR96-058 | 25th November 1996
Dima Grigoriev, Marek Karpinski

Randomized $\mathbf{\Omega (n^2)}$ Lower Bound for Knapsack

We prove $\Omega (n^2)$ complexity \emph{lower bound} for the
general model of \emph{randomized computation trees} solving
the \emph{Knapsack Problem}, and more generally \emph{Restricted
Integer Programming}. This is the \emph{first nontrivial} lower
bound proven for this model of computation. The method of the ... more >>>

TR99-020 | 9th June 1999
Marek Karpinski

Randomized Complexity of Linear Arrangements and Polyhedra

We survey some of the recent results on the complexity of recognizing
n-dimensional linear arrangements and convex polyhedra by randomized
algebraic decision trees. We give also a number of concrete applications
of these results. In particular, we derive first nontrivial, in fact
quadratic, ... more >>>

TR10-133 | 20th August 2010
Parikshit Gopalan, Adam Klivans, Raghu Meka

Polynomial-Time Approximation Schemes for Knapsack and Related Counting Problems using Branching Programs

We give a deterministic, polynomial-time algorithm for approximately counting the number of {0,1}-solutions to any instance of the knapsack problem. On an instance of length n with total weight W and accuracy parameter eps, our algorithm produces a (1 + eps)-multiplicative approximation in time poly(n,log W,1/eps). We also give algorithms ... more >>>

TR12-041 | 17th April 2012
Stasys Jukna

Limitations of Incremental Dynamic Programs

Revisions: 1

We consider so-called ``incremental'' dynamic programming (DP) algorithms, and are interested in the number of subproblems produced by them. The standard DP algorithm for the n-dimensional Knapsack problem is incremental, and produces nK subproblems, where K is the capacity of the knapsack. We show that any incremental algorithm for this ... more >>>

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