arXiv subjects

Peter Manohar

Publications and source records attributed to Peter Manohar.

15 recordsLinked to original sources

arXiv2025

Relaxed vs. Full Local Decodability with Few Queries: Equivalence and Separations for Linear Codes

A locally decodable code (LDC) $C \colon \{0,1\}^k \to \{0,1\}^n$ is an error-correcting code that allows one to recover any bit of the original message with good probability while only reading a small number of bits from a corrupted codeword. A relaxed locally decodable code (RLDC) is a weaker notion where the decoder is additionally allowed to abort and output a special symbol $\bot$ if it detects an error. For a large constant number of queries $q$, there is a large gap between the blocklength $n$ of the best $q$-query LDC and the best $q$-query RLDC. Existing constructions of RLDCs achieve polynomial length $n = k^{1 + O(1/q)}$, while the best-known $q$-LDCs only achieve subexponential length $n = 2^{k^{o(1)}}$. On the other hand, for $q = 2$, it is known that RLDCs and LDCs are equivalent. We thus ask the question: what is the smallest $q$ such that there exists a $q$-RLDC that is not a $q$-LDC? In this work, we show that any linear $3$-query RLDC is in fact a $3$-LDC, i.e., linear RLDCs and LDCs are equivalent at $3$ queries. More generally, we show for any constant $q$, there is a soundness error threshold $s(q)$ such that any linear $q$-RLDC with soundness error below this threshold must be a $q$-LDC. This implies that linear RLDCs cannot have "strong soundness" -- a stricter condition satisfied by linear LDCs that says the soundness error is proportional to the fraction of errors in the corrupted codeword -- unless they are simply LDCs. In addition, we give simple constructions of linear $15$-query RLDCs that are not $q$-LDCs for any constant $q$, showing that for $q = 15$, linear RLDCs and LDCs are not equivalent. We also prove nearly identical results for locally correctable codes and their corresponding relaxed counterpart.

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arXiv2025

Spectral Refutations of Semirandom $k$-LIN over Larger Fields

We study the problem of strongly refuting semirandom $k$-LIN$(\mathbb{F})$ instances: systems of $k$-sparse inhomogeneous linear equations over a finite field $\mathbb{F}$. For the case of $\mathbb{F} = \mathbb{F}_2$, this is the well-studied problem of refuting semirandom instances of $k$-XOR, where the works of [GKM22,HKM23] establish a tight trade-off between runtime and clause density for refutation: for any choice of a parameter $\ell$, they give an $n^{O(\ell)}$-time algorithm to certify that there is no assignment that can satisfy more than $\frac{1}{2} + \varepsilon$-fraction of constraints in a semirandom $k$-XOR instance, provided that the instance has $O(n) \cdot \left(\frac{n}{\ell}\right)^{k/2 - 1} \log n /\varepsilon^4$ constraints, and the work of [KMOW17] provides good evidence that this tight up to a $\mathrm{polylog}(n)$ factor via lower bounds for the Sum-of-Squares hierarchy. However for larger fields, the only known results for this problem are established via black-box reductions to the case of $\mathbb{F}_2$, resulting in an $|{\mathbb{F}}|^{3k}$ gap between the current best upper and lower bounds. In this paper, we give an algorithm for refuting semirandom $k$-LIN$(\mathbb{F})$ instances with the "correct" dependence on the field size $|{\mathbb{F}}|$. For any choice of a parameter $\ell$, our algorithm runs in $(|{\mathbb{F}}|n)^{O(\ell)}$-time and strongly refutes semirandom $k$-LIN$(\mathbb{F})$ instances with at least $O(n) \cdot \left(\frac{|{\mathbb{F}^*}| n}{\ell}\right)^{k/2 - 1} \log(n |{\mathbb{F}^*}|) /\varepsilon^4$ constraints. We give good evidence that this dependence on the field size $|{\mathbb{F}}|$ is optimal by proving a lower bound for the Sum-of-Squares hierarchy that matches this threshold up to a $\mathrm{polylog}(n |{\mathbb{F}^*}|)$ factor. Our results also extend to the more general case of finite Abelian groups.

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arXiv2025

Solving Random Planted CSPs below the $n^{k/2}$ Threshold

We present a family of algorithms to solve random planted instances of any $k$-ary Boolean constraint satisfaction problem (CSP). A randomly planted instance of a Boolean CSP is generated by (1) choosing an arbitrary planted assignment $x^*$, and then (2) sampling constraints from a particular "planting distribution" designed so that $x^*$ will satisfy every constraint. Given an $n$ variable instance of a $k$-ary Boolean CSP with $m$ constraints, our algorithm runs in time $n^{O(\ell)}$ for a choice of a parameter $\ell$, and succeeds in outputting a satisfying assignment if $m \geq O(n) \cdot (n/\ell)^{\frac{k}{2} - 1} \log n$. This generalizes the $\mathrm{poly}(n)$-time algorithm of [FPV15], the case of $\ell = O(1)$, to larger runtimes, and matches the constraint number vs.\ runtime trade-off established for refuting random CSPs by [RRS17]. Our algorithm is conceptually different from the recent algorithm of [GHKM23], which gave a $\mathrm{poly}(n)$-time algorithm to solve semirandom CSPs with $m \geq \tilde{O}(n^{\frac{k}{2}})$ constraints by exploiting conditions that allow a basic SDP to recover the planted assignment $x^*$ exactly. Instead, we forego certificates of uniqueness and recover $x^*$ in two steps: we first use a degree-$O(\ell)$ Sum-of-Squares SDP to find some $\hat{x}$ that is $o(1)$-close to $x^*$, and then we use a second rounding procedure to recover $x^*$ from $\hat{x}$.

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arXiv2024

A $k^{\frac{q}{q-2}}$ Lower Bound for Odd Query Locally Decodable Codes from Bipartite Kikuchi Graphs

A code $C \colon \{0,1\}^k \to \{0,1\}^n$ is a $q$-query locally decodable code ($q$-LDC) if one can recover any chosen bit $b_i$ of the message $b \in \{0,1\}^k$ with good confidence by querying a corrupted string $\tilde{x}$ of the codeword $x = C(b)$ in at most $q$ coordinates. For $2$ queries, the Hadamard code is a $2$-LDC of length $n = 2^k$, and this code is in fact essentially optimal. For $q \geq 3$, there is a large gap in our understanding: the best constructions achieve $n = \exp(k^{o(1)})$, while prior to the recent work of [AGKM23], the best lower bounds were $n \geq \tilde{\Omega}(k^{\frac{q}{q-2}})$ for $q$ even and $n \geq \tilde{\Omega}(k^{\frac{q+1}{q-1}})$ for $q$ odd. The recent work of [AGKM23] used techniques from semirandom XOR refutation to prove a lower bound of $n \geq \tilde{\Omega}(k^3)$ for $q = 3$, thus achieving the "$k^{\frac{q}{q-2}}$ bound" for an odd value of $q$. However, their proof does not extend to any odd $q \geq 5$. In this paper, we prove a $q$-LDC lower bound of $n \geq \tilde{\Omega}(k^{\frac{q}{q-2}})$ for any odd $q$. Our key technical idea is the use of an imbalanced bipartite Kikuchi graph, which gives a simpler method to analyze spectral refutations of odd arity XOR without using the standard "Cauchy-Schwarz trick", a trick that typically produces random matrices with nontrivially correlated entries and makes the analysis for odd arity XOR significantly more complicated than even arity XOR.

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arXiv2024

Exponential Lower Bounds for Smooth 3-LCCs and Sharp Bounds for Designs

We give improved lower bounds for binary $3$-query locally correctable codes (3-LCCs) $C \colon \{0,1\}^k \rightarrow \{0,1\}^n$. Specifically, we prove: (1) If $C$ is a linear design 3-LCC, then $n \geq 2^{(1 - o(1))\sqrt{k} }$. A design 3-LCC has the additional property that the correcting sets for every codeword bit form a perfect matching and every pair of codeword bits is queried an equal number of times across all matchings. Our bound is tight up to a factor $\sqrt{8}$ in the exponent of $2$, as the best construction of binary $3$-LCCs (obtained by taking Reed-Muller codes on $\mathbb{F}_4$ and applying a natural projection map) is a design $3$-LCC with $n \leq 2^{\sqrt{8 k}}$. Up to a $\sqrt{8}$ factor, this resolves the Hamada conjecture on the maximum $\mathbb{F}_2$-codimension of a $4$-design. (2) If $C$ is a smooth, non-linear, adaptive $3$-LCC with perfect completeness, then, $n \geq 2^{\Omega(k^{1/5})}$. (3) If $C$ is a smooth, non-linear, adaptive $3$-LCC with completeness $1 - \varepsilon$, then $n \geq \tilde{\Omega}(k^{\frac{1}{2\varepsilon}})$. In particular, when $\varepsilon$ is a small constant, this implies a lower bound for general non-linear LCCs that beats the prior best $n \geq \tilde{\Omega}(k^3)$ lower bound of [AGKM23] by a polynomial factor. Our design LCC lower bound is obtained via a fine-grained analysis of the Kikuchi matrix method applied to a variant of the matrix used in [KM23]. Our lower bounds for non-linear codes are obtained by designing a from-scratch reduction from nonlinear $3$-LCCs to a system of "chain XOR equations": polynomial equations with similar structure to the long chain derivations that arise in the lower bounds for linear $3$-LCCs [KM23].

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arXiv2023

An Exponential Lower Bound for Linear 3-Query Locally Correctable Codes

We prove that the blocklength $n$ of a linear $3$-query locally correctable code (LCC) $\mathcal{L} \colon {\mathbb F}^k \to {\mathbb F}^n$ with distance $\delta$ must be at least $n \geq 2^{\Omega\left(\left(\frac{\delta^2 k}{(|{\mathbb F}|-1)^2}\right)^{1/8}\right)}$. In particular, the blocklength of a linear $3$-query LCC with constant distance over any small field grows exponentially with $k$. This improves on the best prior lower bound of $n \geq \tilde{\Omega}(k^3)$ [AGKM23], which holds even for the weaker setting of $3$-query locally decodable codes (LDCs), and comes close to matching the best-known construction of $3$-query LCCs based on binary Reed-Muller codes, which achieve $n \leq 2^{O(k^{1/2})}$. Because there is a $3$-query LDC with a strictly subexponential blocklength [Yek08, Efr09], as a corollary we obtain the first strong separation between $q$-query LCCs and LDCs for any constant $q \geq 3$. Our proof is based on a new upgrade of the method of spectral refutations via Kikuchi matrices developed in recent works [GKM22, HKM23, AGKM23] that reduces establishing (non-)existence of combinatorial objects to proving unsatisfiability of associated XOR instances. Our key conceptual idea is to apply this method with XOR instances obtained via long-chain derivations, a structured variant of low-width resolution for XOR formulas from proof complexity [Gri01, Sch08].

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arXiv2023

Efficient Algorithms for Semirandom Planted CSPs at the Refutation Threshold

We present an efficient algorithm to solve semirandom planted instances of any Boolean constraint satisfaction problem (CSP). The semirandom model is a hybrid between worst-case and average-case input models, where the input is generated by (1) choosing an arbitrary planted assignment $x^*$, (2) choosing an arbitrary clause structure, and (3) choosing literal negations for each clause from an arbitrary distribution "shifted by $x^*$" so that $x^*$ satisfies each constraint. For an $n$ variable semirandom planted instance of a $k$-arity CSP, our algorithm runs in polynomial time and outputs an assignment that satisfies all but a $o(1)$-fraction of constraints, provided that the instance has at least $\tilde{O}(n^{k/2})$ constraints. This matches, up to $polylog(n)$ factors, the clause threshold for algorithms that solve fully random planted CSPs [FPV15], as well as algorithms that refute random and semirandom CSPs [AOW15, AGK21]. Our result shows that despite having worst-case clause structure, the randomness in the literal patterns makes semirandom planted CSPs significantly easier than worst-case, where analogous results require $O(n^k)$ constraints [AKK95, FLP16]. Perhaps surprisingly, our algorithm follows a significantly different conceptual framework when compared to the recent resolution of semirandom CSP refutation. This turns out to be inherent and, at a technical level, can be attributed to the need for relative spectral approximation of certain random matrices - reminiscent of the classical spectral sparsification - which ensures that an SDP can certify the uniqueness of the planted assignment. In contrast, in the refutation setting, it suffices to obtain a weaker guarantee of absolute upper bounds on the spectral norm of related matrices.

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arXiv2023

A Near-Cubic Lower Bound for 3-Query Locally Decodable Codes from Semirandom CSP Refutation

A code $C \colon \{0,1\}^k \to \{0,1\}^n$ is a $q$-locally decodable code ($q$-LDC) if one can recover any chosen bit $b_i$ of the message $b \in \{0,1\}^k$ with good confidence by randomly querying the encoding $x := C(b)$ on at most $q$ coordinates. Existing constructions of $2$-LDCs achieve $n = \exp(O(k))$, and lower bounds show that this is in fact tight. However, when $q = 3$, far less is known: the best constructions achieve $n = \exp(k^{o(1)})$, while the best known results only show a quadratic lower bound $n \geq \tilde{\Omega}(k^2)$ on the blocklength. In this paper, we prove a near-cubic lower bound of $n \geq \tilde{\Omega}(k^3)$ on the blocklength of $3$-query LDCs. This improves on the best known prior works by a polynomial factor in $k$. Our proof relies on a new connection between LDCs and refuting constraint satisfaction problems with limited randomness. Our quantitative improvement builds on the new techniques for refuting semirandom instances of CSPs developed in [GKM22, HKM23] and, in particular, relies on bounding the spectral norm of appropriate Kikuchi matrices.

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arXiv2022

Sparsity and $\ell_p$-Restricted Isometry

A matrix $A$ is said to have the $\ell_p$-Restricted Isometry Property ($\ell_p$-RIP) if for all vectors $x$ of up to some sparsity $k$, $\|{Ax}\|_p$ is roughly proportional to $\|{x}\|_p$. We study this property for $m \times n$ matrices of rank proportional to $n$ and $k = \Theta(n)$. In this parameter regime, $\ell_p$-RIP matrices are closely connected to Euclidean sections, and are "real analogs" of testing matrices for locally testable codes. It is known that with high probability, random dense $m\times n$ matrices (e.g., with i.i.d. $\pm 1$ entries) are $\ell_2$-RIP with $k \approx m/\log n$, and sparse random matrices are $\ell_p$-RIP for $p \in [1,2)$ when $k, m = \Theta(n)$. However, when $m = \Theta(n)$, sparse random matrices are known to not be $\ell_2$-RIP with high probability. Against this backdrop, we show that sparse matrices cannot be $\ell_2$-RIP in our parameter regime. On the other hand, for $p \neq 2$, we show that every $\ell_p$-RIP matrix must be sparse. Thus, sparsity is incompatible with $\ell_2$-RIP, but necessary for $\ell_p$-RIP for $p \neq 2$. Under a suitable interpretation, our negative result about $\ell_2$-RIP gives an impossibility result for a certain continuous analog of "$c^3$-LTCs": locally testable codes of constant rate, constant distance and constant locality that were constructed in recent breakthroughs.

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arXiv2022

Bypassing the XOR Trick: Stronger Certificates for Hypergraph Clique Number

Let $\mathcal{H}(k,n,p)$ be the distribution on $k$-uniform hypergraphs where every subset of $[n]$ of size $k$ is included as an hyperedge with probability $p$ independently. In this work, we design and analyze a simple spectral algorithm that certifies a bound on the size of the largest clique, $\omega(H)$, in hypergraphs $H \sim \mathcal{H}(k,n,p)$. For example, for any constant $p$, with high probability over the choice of the hypergraph, our spectral algorithm certifies a bound of $\tilde{O}(\sqrt{n})$ on the clique number in polynomial time. This matches, up to $\textrm{polylog}(n)$ factors, the best known certificate for the clique number in random graphs, which is the special case of $k = 2$. Prior to our work, the best known refutation algorithms [CGL04, AOW15] rely on a reduction to the problem of refuting random $k$-XOR via Feige's XOR trick [Fei02], and yield a polynomially worse bound of $\tilde{O}(n^{3/4})$ on the clique number when $p = O(1)$. Our algorithm bypasses the XOR trick and relies instead on a natural generalization of the Lovasz theta semidefinite programming relaxation for cliques in hypergraphs.

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arXiv2021

Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally

In this work, we revisit the problem of estimating the mean and covariance of an unknown $d$-dimensional Gaussian distribution in the presence of an $\varepsilon$-fraction of adversarial outliers. The pioneering work of [DKK+16] gave a polynomial time algorithm for this task with optimal $\tilde{O}(\varepsilon)$ error using $n = \textrm{poly}(d, 1/\varepsilon)$ samples. On the other hand, [KS17b] introduced a general framework for robust moment estimation via a canonical sum-of-squares relaxation that succeeds for the more general class of certifiably subgaussian and certifiably hypercontractive [BK20] distributions. When specialized to Gaussians, this algorithm obtains the same $\tilde{O}(\varepsilon)$ error guarantee as [DKK+16] but incurs a super-polynomial sample complexity ($n = d^{O(\log(1/\varepsilon)}$) and running time ($n^{O(\log(1/\varepsilon))}$). This cost appears inherent to their analysis as it relies only on sum-of-squares certificates of upper bounds on directional moments while the analysis in [DKK+16] relies on lower bounds on directional moments inferred from algebraic relationships between moments of Gaussian distributions. We give a new, simple analysis of the same canonical sum-of-squares relaxation used in [KS17b, BK20] and show that for Gaussian distributions, their algorithm achieves the same error, sample complexity and running time guarantees as of the specialized algorithm in [DKK+16]. Our key innovation is a new argument that allows using moment lower bounds without having sum-of-squares certificates for them. We believe that our proof technique will likely be useful in developing further robust estimation algorithms.

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arXiv2021

Algorithms and Certificates for Boolean CSP Refutation: "Smoothed is no harder than Random"

We present an algorithm for strongly refuting smoothed instances of all Boolean CSPs. The smoothed model is a hybrid between worst and average-case input models, where the input is an arbitrary instance of the CSP with only the negation patterns of the literals re-randomized with some small probability. For an $n$-variable smoothed instance of a $k$-arity CSP, our algorithm runs in $n^{O(\ell)}$ time, and succeeds with high probability in bounding the optimum fraction of satisfiable constraints away from $1$, provided that the number of constraints is at least $\tilde{O}(n) (\frac{n}{\ell})^{\frac{k}{2} - 1}$. This matches, up to polylogarithmic factors in $n$, the trade-off between running time and the number of constraints of the state-of-the-art algorithms for refuting fully random instances of CSPs [RRS17]. We also make a surprising new connection between our algorithm and even covers in hypergraphs, which we use to positively resolve Feige's 2008 conjecture, an extremal combinatorics conjecture on the existence of even covers in sufficiently dense hypergraphs that generalizes the well-known Moore bound for the girth of graphs. As a corollary, we show that polynomial-size refutation witnesses exist for arbitrary smoothed CSP instances with number of constraints a polynomial factor below the "spectral threshold" of $n^{k/2}$, extending the celebrated result for random 3-SAT of Feige, Kim and Ofek [FKO06].

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arXiv2021

$\ell_p$-Spread and Restricted Isometry Properties of Sparse Random Matrices

Random subspaces $X$ of $\mathbb{R}^n$ of dimension proportional to $n$ are, with high probability, well-spread with respect to the $\ell_2$-norm. Namely, every nonzero $x \in X$ is "robustly non-sparse" in the following sense: $x$ is $\varepsilon \|x\|_2$-far in $\ell_2$-distance from all $\delta n$-sparse vectors, for positive constants $\varepsilon, \delta$ bounded away from $0$. This "$\ell_2$-spread" property is the natural counterpart, for subspaces over the reals, of the minimum distance of linear codes over finite fields, and corresponds to $X$ being a Euclidean section of the $\ell_1$ unit ball. Explicit $\ell_2$-spread subspaces of dimension $\Omega(n)$, however, are unknown, and the best known constructions (which achieve weaker spread properties), are analogs of low density parity check (LDPC) codes over the reals, i.e., they are kernels of sparse matrices. We study the spread properties of the kernels of sparse random matrices. Rather surprisingly, we prove that with high probability such subspaces contain vectors $x$ that are $o(1)\cdot \|x\|_2$-close to $o(n)$-sparse with respect to the $\ell_2$-norm, and in particular are not $\ell_2$-spread. On the other hand, for $p < 2$ we prove that such subspaces are $\ell_p$-spread with high probability. Moreover, we show that a random sparse matrix has the stronger restricted isometry property (RIP) with respect to the $\ell_p$ norm, and this follows solely from the unique expansion of a random biregular graph, yielding a somewhat unexpected generalization of a similar result for the $\ell_1$ norm [BGI+08]. Instantiating this with explicit expanders, we obtain the first explicit constructions of $\ell_p$-RIP matrices for $1 \leq p < p_0$, where $1 < p_0 < 2$ is an absolute constant.

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arXiv2021

A Stress-Free Sum-of-Squares Lower Bound for Coloring

We prove that with high probability over the choice of a random graph $G$ from the Erd\H{o}s-R\'enyi distribution $G(n,1/2)$, a natural $n^{O(\varepsilon^2 \log n)}$-time, degree $O(\varepsilon^2 \log n)$ sum-of-squares semidefinite program cannot refute the existence of a valid $k$-coloring of $G$ for $k = n^{1/2 +\varepsilon}$. Our result implies that the refutation guarantee of the basic semidefinite program (a close variant of the Lov\'asz theta function) cannot be appreciably improved by a natural $o(\log n)$-degree sum-of-squares strengthening, and this is tight up to a $n^{o(1)}$ slack in $k$. To the best of our knowledge, this is the first lower bound for coloring $G(n,1/2)$ for even a single round strengthening of the basic SDP in any SDP hierarchy. Our proof relies on a new variant of instance-preserving non-pointwise complete reduction within SoS from coloring a graph to finding large independent sets in it. Our proof is (perhaps surprisingly) short, simple and does not require complicated spectral norm bounds on random matrices with dependent entries that have been otherwise necessary in the proofs of many similar results [BHK+16, HKP+17, KB19, GJJ+20, MRX20]. Our result formally holds for a constraint system where vertices are allowed to belong to multiple color classes; we leave the extension to the formally stronger formulation of coloring, where vertices must belong to unique colors classes, as an outstanding open problem.

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arXiv2020

Lower Bounds for Caching with Delayed Hits

Caches are a fundamental component of latency-sensitive computer systems. Recent work of [ASWB20] has initiated the study of delayed hits: a phenomenon in caches that occurs when the latency between the cache and backing store is much larger than the time between new requests. We present two results for the delayed hits caching model. (1) Competitive ratio lower bound. We prove that the competitive ratio of the algorithm in [ASWB20], and more generally of any deterministic online algorithm for delayed hits, is at least Omega(kZ), where k is the cache size and Z is the delay parameter. (2) Antimonotonicity of the delayed hits latency. Antimonotonicity is a naturally desirable property of cache latency: having a cache hit instead of a cache miss should result in lower overall latency. We prove that the latency of the delayed hits model is not antimonotone by exhibiting a scenario where having a cache hit instead of a miss results in an increase in overall latency. We additionally present a modification of the delayed hits model that makes the latency antimonotone.

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