Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size


  • Christoph Hertrich Institute of Mathematics Technische Universität Berlin
  • Martin Skutella Institute of Mathematics Technische Universität Berlin



(Deep) Neural Network Learning Theory, (Deep) Neural Network Algorithms, Optimization, Other Foundations of Machine Learning


The development of a satisfying and rigorous mathematical understanding of the performance of neural networks is a major challenge in artificial intelligence. Against this background, we study the expressive power of neural networks through the example of the classical NP-hard Knapsack Problem. Our main contribution is a class of recurrent neural networks (RNNs) with rectified linear units that are iteratively applied to each item of a Knapsack instance and thereby compute optimal or provably good solution values. We show that an RNN of depth four and width depending quadratically on the profit of an optimum Knapsack solution is sufficient to find optimum Knapsack solutions. We also prove the following tradeoff between the size of an RNN and the quality of the computed Knapsack solution: for Knapsack instances consisting of n items, an RNN of depth five and width w computes a solution of value at least 1 - O(n^2 sqrt(w)) times the optimum solution value. Our results build upon a classical dynamic programming formulation of the Knapsack Problem as well as a careful rounding of profit values that are also at the core of the well-known fully polynomial-time approximation scheme for the Knapsack Problem. Finally, we point out that our results can be generalized to many other combinatorial optimization problems that admit dynamic programming solution methods, such as various Shortest Path Problems, the Longest Common Subsequence Problem, and the Traveling Salesperson Problem.




How to Cite

Hertrich, C., & Skutella, M. (2021). Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 7685-7693.



AAAI Technical Track on Machine Learning II