## Abstract

We introduce and study a discrete multi-period extension of the classical knapsack problem, dubbed generalized incremental knapsack. In this setting, we are given a set of n items, each associated with a non-negative weight, and T time periods with non-decreasing capacities W_{1}≤ ⋯ ≤ W_{T}. When item i is inserted at time t, we gain a profit of p_{it}; however, this item remains in the knapsack for all subsequent periods. The goal is to decide if and when to insert each item, subject to the time-dependent capacity constraints, with the objective of maximizing our total profit. Interestingly, this setting subsumes as special cases a number of recently-studied incremental knapsack problems, all known to be strongly NP-hard. Our first contribution comes in the form of a polynomial-time (12-ϵ)-approximation for the generalized incremental knapsack problem. This result is based on a reformulation as a single-machine sequencing problem, which is addressed by blending dynamic programming techniques and the classical Shmoys–Tardos algorithm for the generalized assignment problem. Combined with further enumeration-based self-reinforcing ideas and new structural properties of nearly-optimal solutions, we turn our algorithm into a quasi-polynomial time approximation scheme (QPTAS). Hence, under widely believed complexity assumptions, this finding rules out the possibility that generalized incremental knapsack is APX-hard.

Original language | English |
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Pages (from-to) | 27-83 |

Number of pages | 57 |

Journal | Mathematical Programming |

Volume | 198 |

Issue number | 1 |

DOIs | |

State | Published - Mar 2023 |

## Keywords

- Approximation algorithms
- Incremental optimization
- Sequencing

## All Science Journal Classification (ASJC) codes

- Software
- General Mathematics