Inapproximability of Sparsest Vector in a Real Subspace

2024-10-03

Electron. Colloquium Comput. Complex.

AI Summary

This work investigates the computational complexity of sparse vector search in real subspaces, lattices, and codes, establishing— for the first time over the reals—strong inapproximability results. Methodologically, it bypasses the PCP theorem and introduces a novel tensorized code product test, tightly integrating Littlewood–Offord theory with the Rudelson–Vershynin anti-concentration inequality, alongside kernel tensorization of ±1 random matrices and real embedding of quadratic equations. The main contribution is a proof that approximating the sparsest nonzero vector in a real subspace within any constant factor is NP-hard under randomized reductions. As a corollary, it yields the current best inapproximability bound for the Shortest Vector Problem (SVP) on lattices. This framework provides a unified hardness analysis paradigm for fundamental problems including real-domain sparse optimization, small-set expansion, and quantum separability.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Matrix & Tensor Methods
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We establish strong inapproximability for finding the sparsest nonzero vector in a real subspace. We show that it is NP-Hard (under randomized reductions) to approximate the sparsest vector in a subspace within any constant factor (or almost polynomial factors in quasipolynomial time). We recover as a corollary state of the art inapproximability for the shortest vector problem (SVP), a foundational problem in lattice based cryptography. Our proof is surprisingly simple, bypassing even the PCP theorem. We are inspired by the homogenization framework from the inapproximability theory of minimum distance problems (MDC) in integer lattices and error correcting codes. We use a combination of (a) emph{product testing via tensor codes} and (b) emph{encoding an assignment as a coset of a random code in higher dimensional space} in order to embed non-homogeneous quadratic equations into the sparsest vector problem. (a) is inspired by Austrin and Khot's simplified proof of hardness of MDC over finite fields, and (b) is inspired by Micciancio's semi-derandomization of hardness of SVP. Our reduction involves the challenge of performing (a) over the reals. We prove that tensoring of the kernel of a +1/-1 random matrix furnishes an adequate product test (while still allowing (b)). The proof exposes a connection to Littlewood-Offord theory and relies on a powerful anticoncentration result of Rudelson and Vershynin. Our main motivation in this work is the development of inapproximability theory for problems over the reals. Analytic variants of sparsest vector have connections to small set expansion, quantum separability and polynomial maximization over convex sets, all of which cause similar barriers to inapproximability. The approach we develop could lead to progress on the hardness of some of these problems.

Problem

• Proving strong inapproximability for sparse vector problems
• Extending hardness results to all p-norms where p≥0
• Developing techniques for real-number inapproximability problems

Innovation

• Novel approach bypassing PCP theorem
• Reduction with Boolean solution property
• Extends hardness to all p-norms p>=0