Algebraic Methods in Computational Complexity

Computational Complexity is concerned with the resources that are required for algorithms to detect properties of combinatorial objects and structures. It has often proven true that the best way to argue about these combinatorial objects is by establishing a connection (perhaps approximate) to a more well-behaved algebraic setting. Indeed, many of the deepest and most powerful results in Computational Complexity rely on algebraic proof techniques. The Razborov-Smolensky polynomial-approximation method for proving constant-depth circuit lower bounds, the PCP characterization of NP, and the Agrawal-Kayal-Saxena polynomial-time primality test are some of the most prominent examples. In some of the most exciting recent progress in Computational Complexity the algebraic theme still plays a central role. There have been significant recent advances in algebraic circuit lower bounds, and the so-called chasm at depth 4 suggests that the restricted models now being considered are not so far from ones that would lead to a general result. There have been similar successes concerning the related problems of polynomial identity testing and circuit reconstruction in the algebraic model (and these are tied to central questions regarding the power of randomness in computation). Also the areas of derandomization and coding theory have experimented important advances. The seminar aimed to capitalize on recent progress and bring together researchers who are using a diverse array of algebraic methods in a variety of settings. Researchers in these areas are relying on ever more sophisticated and specialized mathematics and the goal of the seminar was to play an important role in educating a diverse community about the latest new techniques

Levin–Kolmogorov Complexity is not in Linear Time

Understanding the computational hardness of Kolmogorov complexity is a central open question in complexity theory. An important notion is Levin\u27s version of Kolmogorov complexity, Kt, and its decisional variant, MKtP, due to its connections to universal search, derandomization, and oneway functions, among others. The question whether MKtP can be computed in polynomial time is particularly interesting because it is not subject to known technical barriers such as algebrization or natural proofs that would explain the lack of a proof for MKtP not in P. We take a significant step towards proving MKtP not in P by developing an algorithmic approach for showing unconditionally that MKtP not in DTIME[O(n)] cannot be decided in deterministic linear time in the worst-case. This allows us to partially affirm a conjecture by Ren and Santhanam [STACS:RS22] about a non-halting variant of Kt complexity. Additionally, we give conditional lower bounds for MKtP that tolerate either more runtime or one-sided error

The non-hardness of approximating circuit size

The Minimum Circuit Size Problem (MCSP) has been the focus of intense study recently; MCSP is hard for SZK under rather powerful reductions, and is provably not hard under “local” reductions computable in TIME(n^0.49). The question of whether MCSP is NP-hard (or indeed, hard even for small subclasses of P) under some of the more familiar notions of reducibility (such as many-one or Turing reductions computable in polynomial time or in AC0) is closely related to many of the longstanding open questions in complexity theory. All prior hardness results for MCSP hold also for computing somewhat weak approximations to the circuit complexity of a function. Some of these results were proved by exploiting a connection to a notion of time-bounded Kolmogorov complexity (KT) and the corresponding decision problem (MKTP). More recently, a new approach for proving improved hardness results for MKTP was developed, but this approach establishes only hardness of extremely good approximations of the form 1+o(1), and these improved hardness results are not yet known to hold for MCSP. In particular, it is known that MKTP is hard for the complexity class DET under nonuniform AC0 many-one reductions, implying MKTP is not in AC0[p] for any prime p. It was still open if similar circuit lower bounds hold for MCSP. One possible avenue for proving a similar hardness result for MCSP would be to improve the hardness of approximation for MKTP beyond 1 + o(1) to omega(1), as KT-complexity and circuit size are polynomially-related. In this paper, we show that this approach cannot succeed. More specifically, we prove that PARITY does not reduce to the problem of computing superlinear approximations to KT-complexity or circuit size via AC0-Turing reductions that make O(1) queries. This is significant, since approximating any set in P/poly AC0-reduces to just one query of a much worse approximation of circuit size or KT-complexity. For weaker approximations, we also prove non-hardness under more powerful reductions. Our non-hardness results are unconditional, in contrast to conditional results presented in [7] (for more powerful reductions, but for much worse approximations). This highlights obstacles that would have to be overcome by any proof that MKTP or MCSP is hard for NP under AC0 reductions. It may also be a step toward confirming a conjecture of Murray and Williams, that MCSP is not NP-complete under logtime-uniform AC0 many-one reductions.Peer reviewe

The Non-hardness of Approximating Circuit Size

Abstract The Minimum Circuit Size Problem (MCSP) has been the focus of intense study recently; MCSP is hard for SZK under rather powerful reductions (Allender and Das Inf. Comput. 256, 2–8, 2017), and is provably not hard under “local” reductions computable in TIME(n0.49) (Murray and Williams Theory Comput. 13(1), 1–22, 2017). The question of whether MCSP is NP-hard (or indeed, hard even for small subclasses of P) under some of the more familiar notions of reducibility (such as many-one or Turing reductions computable in polynomial time or in AC0) is closely related to many of the longstanding open questions in complexity theory (Allender and Hirahara ACM Trans. Comput. Theory 11(4), 27:1–27:27, 2019; Allender et al. Comput. Complex. 26(2), 469–496, 2017; Hirahara and Santhanam 2017; Hirahara and Watanabe 2016; Hitchcock and Pavan 2015; Impagliazzo et al. 2018; Murray and Williams Theory Comput. 13(1), 1–22, 2017). All prior hardness results for MCSP hold also for computing somewhat weak approximations to the circuit complexity of a function (Allender et al. SIAM J. Comput. 35(6), 1467–1493, 2006; Allender and Das Inf. Comput. 256, 2–8, 2017; Allender et al. J. Comput. Syst. Sci. 77(1), 14–40, 2011; Hirahara and Santhanam 2017; Kabanets and Cai 2000; Rudow Inf. Process. Lett. 128, 1–4, 2017) (Subsequent to our work, a new hardness result has been announced (Ilango 2020) that relies on more exact size computations). Some of these results were proved by exploiting a connection to a notion of time-bounded Kolmogorov complexity (KT) and the corresponding decision problem (MKTP). More recently, a new approach for proving improved hardness results for MKTP was developed (Allender et al. SIAM J. Comput. 47(4), 1339–1372, 2018; Allender and Hirahara ACM Trans. Comput. Theory 11(4), 27:1–27:27, 2019), but this approach establishes only hardness of extremely good approximations of the form 1 + o(1), and these improved hardness results are not yet known to hold for MCSP. In particular, it is known that MKTP is hard for the complexity class DET under nonuniform ≤ m A C 0 $\leq _{\text {m}}^{\mathsf {AC}^{0}}$ reductions, implying MKTP is not in AC0[p] for any prime p (Allender and Hirahara ACM Trans. Comput. Theory 11(4), 27:1–27:27, 2019). It was still open if similar circuit lower bounds hold for MCSP (But see Golovnev et al. 2019; Ilango 2020). One possible avenue for proving a similar hardness result for MCSP would be to improve the hardness of approximation for MKTP beyond 1 + o(1) to ω(1), as KT-complexity and circuit size are polynomially-related. In this paper, we show that this approach cannot succeed. More specifically, we prove that PARITY does not reduce to the problem of computing superlinear approximations to KT-complexity or circuit size via AC0-Turing reductions that make O(1) queries. This is significant, since approximating any set in P/poly AC0-reduces to just one query of a much worse approximation of circuit size or KT-complexity (Oliveira and Santhanam 2017). For weaker approximations, we also prove non-hardness under more powerful reductions. Our non-hardness results are unconditional, in contrast to conditional results presented in Allender and Hirahara (ACM Trans. Comput. Theory 11(4), 27:1–27:27, 2019) (for more powerful reductions, but for much worse approximations). This highlights obstacles that would have to be overcome by any proof that MKTP or MCSP is hard for NP under AC0 reductions. It may also be a step toward confirming a conjecture of Murray and Williams, that MCSP is not NP-complete under logtime-uniform ≤ m A C 0 $\leq _{\text {m}}^{\mathsf {AC}^{0}}$ reductions