Solving low-density subset sum problems
Journal of the ACM (JACM)
On the Lagarias-Odlyzko algorithm for the subset sum problem
SIAM Journal on Computing
Minkowski's convex body theorem and integer programming
Mathematics of Operations Research
Succinct certificates for almost all subset sum problems
SIAM Journal on Computing
The generalized basis reduction algorithm
Mathematics of Operations Research
Solving a System of Linear Diophantine Equations with Lower and Upper Bounds on the Variables
Mathematics of Operations Research
A Class of Hard Small 0-1 Programs
INFORMS Journal on Computing
Lifted Cover Inequalities for 0-1 Integer Programs: Complexity
INFORMS Journal on Computing
Market Split and Basis Reduction: Towards a Solution of the Cornuéjols-Dawande Instances
INFORMS Journal on Computing
Hard Equality Constrained Integer Knapsacks
Mathematics of Operations Research
Early Estimates of the Size of Branch-and-Bound Trees
INFORMS Journal on Computing
Column basis reduction and decomposable knapsack problems
Discrete Optimization
New three-level resource management enhancing quality of offline hardware task placement on FPGA
International Journal of Reconfigurable Computing
Throughput and energy-aware routing for 802.11 based mesh networks
Computer Communications
The Cunningham-Geelen Method in Practice: Branch-Decompositions and Integer Programming
INFORMS Journal on Computing
Integer feasibility of random polytopes: random integer programs
Proceedings of the 5th conference on Innovations in theoretical computer science
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The classical branch-and-bound algorithm for the integer feasibility problem [EQUATION] has exponential worst case complexity. We prove that it is surprisingly efficient on reformulations of (0.1), in which the columns of the constraint matrix are short and near orthogonal, i.e., a reduced basis of the generated lattice: when the entries of A are from {1, ..., M} for a large enough M, branch-and-bound solves almost all reformulated instances at the root node. For all A matrices we prove an upper bound on the width of the reformulations along the last unit vector. Our results generalize the results of Furst and Kannan on the solvability of subset sum problems; also, we prove them via branch-and-bound, an algorithm traditionally considered inefficient from the theoretical point of view. We use two main tools: first, we find a bound on the size of the branch-and-bound tree based on the norms of the Gram-Schmidt vectors of the constraint matrix. Second, building on the ideas of Furst and Kannan, we bound the number of integral matrices for which the shortest nonzero vectors of certain lattices are long. We explore practical aspects of these results. We compute numerical values of M which guarantee that 90 and 99 percent of the reformulated problems solve at the root: these turn out to be surprisingly small when the problem size is moderate. We also confirm with a computational study that random integer programs become easier, as the coefficients grow.