Polynomial Time Algorithms for Integer Programming and Unbounded Subset Sum in the Total Regime (2407.05435v2)

Abstract: The Unbounded Subset Sum (USS) problem is an NP-hard computational problem where the goal is to decide whether there exist non-negative integers $x_1, \ldots, x_n$ such that $x_1 a_1 + \ldots + x_n a_n = b$, where $a_1 < \cdots < a_n < b$ are distinct positive integers with $\text{gcd}(a_1, \ldots, a_n)$ dividing $b$. The problem can be solved in pseudopolynomial time, while specialized cases, such as when $b$ exceeds the Frobenius number of $a_1, \ldots, a_n$ simplify to a total problem where a solution always exists. This paper explores the concept of totality in USS. The challenge in this setting is to actually find a solution, even though we know its existence is guaranteed. We focus on the instances of USS where solutions are guaranteed for large $b$. We show that when $b$ is slightly greater than the Frobenius number, we can find the solution to USS in polynomial time. We then show how our results extend to Integer Programming with Equalities (ILPE), highlighting conditions under which ILPE becomes total. We investigate the diagonal Frobenius number, which is the appropriate generalization of the Frobenius number to this context. In this setting, we give a polynomial-time algorithm to find a solution of ILPE. The bound obtained from our algorithmic procedure for finding a solution almost matches the recent existential bound of Bach, Eisenbrand, Rothvoss, and Weismantel (2024).

• The paper presents a novel polynomial-time algorithm for solving Unbounded Subset Sum and ILPE problems under guaranteed total regime conditions.
• The algorithm leverages the diagonal Frobenius number and efficient lattice reduction techniques to establish feasibility and improve computational bounds.
• This research offers practical insights by tightening algorithmic bounds, reducing complexity, and advancing theoretical understanding in integer programming.

Polynomial Time Algorithms for Integer Programming and Unbounded Subset Sum in the Total Regime

The paper "Polynomial Time Algorithms for Integer Programming and Unbounded Subset Sum in the Total Regime" by Divesh Aggarwal, Antoine Joux, Miklos Santha, and Karol Węgrzycki explores novel algorithmic strategies to tackle the Unbounded Subset Sum (USS) problem and its generalizations in Integer Linear Programming with Equalities (ILPE). The key focus is on instances described as "total problems," where the existence of a solution is guaranteed, and the challenge lies in constructing an efficient, polynomial-time method to find such a solution.

Unbounded Subset Sum (USS) Problem

The USS problem is a combinatorial problem modeled as finding non-negative integers $x_1, \ldots, x_n$ such that $x_1 a_1 + \ldots + x_n a_n = b$, given distinct positive integers $a_1 < \ldots < a_n < b$. The problem is NP-hard and has been traditionally solved in pseudopolynomial time. This research addresses the solution of USS when the target $b$ is significantly larger than the Frobenius number of $\{a_1, \ldots, a_n\}$.

Main Contributions for USS

1. Polynomial Time Algorithm: The paper provides a polynomial-time algorithm for solving USS when $b$ is slightly greater than the Frobenius number. Specifically, the algorithm is efficient when $b \geq \frac{a_n^2}{i-1}$ for values $k < i \leq n$.
2. Inductive Approach: The algorithm relies on an inductive strategy that reduces the problem size step-by-step by focusing on subsets of input while ensuring that a feasible solution is derived.

Integer Linear Programming with Equalities (ILPE)

ILPE generalizes the USS problem to higher dimensions, stated as finding $x \in \mathbb{Z}^n$ such that $A x = b$, where $A \in \mathbb{Z}^{d \times n}$ and $b \in \mathbb{Z}^d$. The paper extends the methodology used for USS to ILPE and introduces the concept of the diagonal Frobenius number as a condition ensuring the existence of solutions.

Main Contributions for ILPE

1. Diagonal Frobenius Number: The authors introduce conditions under which ILPE becomes a total problem and provide a polynomial-time algorithm to find solutions.
2. Generalization of Frobenius Numbers: The developed algorithms leverage the diagonal Frobenius number for ensuring totality and for robust solution derivation in polynomial time.
3. Algorithmic Bound: The work offers an algorithmic bound that closely matches or improves upon previous known existential bounds, specifically, the polynomial-time computable upper bound conditions for ILPE outlined by Bach et al. (2024).

Implications of the Research

The research has both practical and theoretical implications:

• Efficiency in Problem Solving: For practitioners, the polynomial-time algorithms reduce computational complexity in USS and ILPE instances wherein solutions are guaranteed by specific large bounds on the target value.
• Theoretical Advances: The introduction of the diagonal Frobenius number opens avenues for deeper exploration and understanding of integer programming problems, aligning more problems within the total regime.
• Algorithmic Techniques: Enhanced algorithmic methods, like the inductive approach and lattice basis reduction, provide new tools for NP-hard problem cases that yield under total problem conditions.

Future Developments and Speculation

This work lays a foundation for future improvements in both USS and ILPE domains. Prospective advancements might include:

• Refinement of Bounds: Further tightening of the conditions required for polynomial-time solvability, potentially lowering the threshold for $b$ in USS.
• Broad Applicability: Extending the current framework to more complex ILP forms, including mixed integer linear programming and other combinatorial optimization problems.
• Algorithm Optimization: Leveraging advanced lattice reduction techniques or heuristic optimizations to further enhance the efficiency and applicability of the proposed algorithms.

In summary, this paper establishes significant advancements in the polynomial-time solvability of certain instances of USS and ILPE problems, providing robust theoretical insights and practical algorithms for total problem regimes in computational theory.