A Tight Double-Exponentially Lower Bound for High-Multiplicity Bin Packing
By: Klaus Jansen, Felix Ohnesorge, Lis Pirotton
Potential Business Impact:
Proves computers can't pack items much faster.
Consider a high-multiplicity Bin Packing instance $I$ with $d$ distinct item types. In 2014, Goemans and Rothvoss gave an algorithm with runtime ${{|I|}^2}^{O(d)}$ for this problem~[SODA'14], where $|I|$ denotes the encoding length of the instance $I$. Although, Jansen and Klein~[SODA'17] later developed an algorithm that improves upon this runtime in a special case, it has remained a major open problem by Goemans and Rothvoss~[J.ACM'20] whether the doubly exponential dependency on $d$ is necessary. We solve this open problem by showing that unless the ETH fails, there is no algorithm solving the high-multiplicity Bin Packing problem in time ${{|I|}^2}^{o(d)}$. To prove this, we introduce a novel reduction from 3-SAT. The core of our construction is efficiently encoding the entire information from a 3-SAT instance with $n$ variables into an ILP with $O(\log(n))$ variables. This result confirms that the Goemans and Rothvoss algorithm is best-possible for Bin Packing parameterized by the number $d$ of item sizes.
Similar Papers
The Support of Bin Packing is Exponential
Data Structures and Algorithms
Finds the best way to pack items.
Robust Extensible Bin Packing and Revisiting the Convex Knapsack Problem
Discrete Mathematics
Organizes items into boxes better, saving space.
An ETH-Tight FPT Algorithm for Rejection-Proof Set Packing with Applications to Kidney Exchange
Data Structures and Algorithms
Helps match more kidney donors and patients.