arXiv subjects

Jean-Guillaume Dumas

Publications and source records attributed to Jean-Guillaume Dumas.

At least 19 recordsLinked to original sources

arXiv2026

Fast matrix multiplication via recursive $\langle$ 4x4x4:48 $\rangle$ algorithms into practice

We present a fast algorithm for multiplying two 4x4 matrices using 48 multiplications and 204 other operations (addition, subtraction or scaling by a constant) over any ring containing an inverse of 2. Applied recursively, this algorithm reaches a cost bound with leading term (7+3/8)n^{\log_4(48)}. Alternative basis decompositions of this algorithm further reduce this to (6+13/32)n^{\log_4(48)}, with square change of bases, and then to (5+2/3)n^{\log_4(48)}, using rectangular encodings with inner dimensions 24 and 32. The conversion costs of these alternative basis variants are included as lower-order terms.

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arXiv2026

A more accurate rational non-commutative algorithm for multiplying 4x4 matrices using 48 multiplications

We propose a more accurate variant of an algorithm for multiplying 4x4 matrices using 48 multiplications over any ring containing an inverse of 2. This algorithm achieves an error bound exponent of only log4($γ$$\infty$,2) $\approx$2.335. In practice, it also reaches a better accuracy w.r.t. max-norm, when compared to previously known such fast algorithms. Furthermore, we propose a straight line program of this algorithm, giving a leading constant in its complexity bound of $316/32 n^{2+log\_4(3)} + o(n^{2+log\_4(3))$ operations over any ring containing an inverse of 2.

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arXiv2026

A non-commutative algorithm for multiplying 4x4 matrices using 48 non-complex multiplications

The quest for non-commutative matrix multiplication algorithms over non-commutative rings in small dimensions has recently seen significant progress. Specifically, the number of scalar multiplications required to multiply two 4x4 matrices was reduced in \cite{Fawzi:2022aa} from 49 (using two recursion levels of Strassen's algorithm) to 47 in characteristic 2, and more recently to 48 in \cite{alphaevolve} over the complex numbers. We propose an algorithm requiring 48 multiplications that uses only rational coefficients, thereby removing the requirement for complex-number arithmetic, and making this algorithm valid over any ring except those of characteristic 2. We also produce a straight-line program of this algorithm reducing the number of additions and scalar multiplications, reaching a running time of $\frac{347}{32}n^{2+\log_4{3}}+o(n^{2+\log_4{3}})$ operations, as well as an alternative basis variant of it, leading to an algorithm running in $7n^{2+\log_4{3}} +o(n^{2+\log_4{3}})$ operations over any ring containing an inverse of 2. Similarly, the number of scalar multiplications required to multiply a 3x4 matrix by a 4x7 matrix was reduced from 66 in \cite{Smirnov:2021aa} to 63 in \cite{alphaevolve} by an algorithm over complex numbers. Using the same techniques, we propose an equivalent algorithm in 63 multiplications using only rational coefficients. In both cases the rational algorithm is obtained by identifying an isotropy that projects the previously known complex-valued decomposition onto the field of rational numbers.

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arXiv2026

Computational Explorations on Semifields

A finite semifield is a division algebra over a finite field where multiplication is not necessarily associative. We consider here the complexity of the multiplication in small semifields and finite field extensions. For this operation, the number of required base field multiplications is the tensor rank, or the multiplicative complexity. The other base field operations are additions and scalings by constants, which together we refer to as the additive complexity. When used recursively, the tensor rank determines the exponent while the other operations determine the constant of the associated asymptotic complexity bounds. For small extensions, both measures are of similar importance. In this paper, we establish the tensor rank of some semifields and finite fields of characteristics 2 and 3. We also propose new upper and lower bounds on their additive complexity, and give new associated algorithms improving on the state-of-the-art in terms of overall complexity. We achieve this by considering short straight line programs for encoding linear codes with given parameters.

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arXiv2026

Algorithms for the local and the global postage stamp problem

We consider stamps with different values (denominations) and same dimensions, and an envelope with a fixed maximum number of stamp positions. The local postage stamp problem is to find the smallest value that cannot be realized by the sum of the stamps on the envelope. The global postage stamp problem is to find the set of denominations that maximize that smallest value for a fixed number of distinct denominations. The local problem is NP-hard and we propose here a novel algorithm that improves on both the time complexity bound and the amount of required memory. We also propose a polynomial approximation algorithm for the global problem together with its complexity analysis. Finally we show that our algorithms allow to improve secure multi-party computations on sets via a more efficient homomorphic evaluation of polynomials on ciphered values.

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arXiv2026

Fuzzy Private Set Union via Oblivious Key Homomorphic Encryption Retrieval

Private Set Multi-Party Computations are protocols that allow parties to jointly and securely compute functions: apart from what is deducible from the output of the function, the input sets are kept private. Then, a Private Set Union (PSU), resp. Intersection (PSI), is a protocol that allows parties to jointly compute the union, resp. the intersection, between their private sets. Now a structured PSI, is a PSI where some structure of the sets can allow for more efficient protocols. For instance in Fuzzy PSI, elements only need to be close enough, instead of equal, to be part of the intersection. We present in this paper, Fuzzy PSU protocols (FPSU), able to efficiently take into account approximations in the union. For this, we introduce a new efficient sub-protocol, called Oblivious Key Homomorphic Encryption Retrieval (OKHER), improving on Oblivious Key-Value Retrieval (OKVR) techniques in our setting. In the fuzzy context, the receiver set $X=\{x_i\}_{1..n}$ is replaced by ${\mathcal B}_δ(X)$, the union of $n$ balls of dimension $d$ with radius $δ$, centered at the $x_i$. The sender set is just its $m$ points of dimension $d$. Then the FPSU functionality corresponds to $X \sqcup \{y \in Y, y \notin {\mathcal B}_δ(X)\}$. Thus, we formally define the FPSU functionality and security properties, and propose several protocols tuned to the patterns of the balls using the $l_\infty$ distance. Using our OKHER routine and homomorphic encryption, we are for instance able to obtain a FPSU protocols with an asymptotic communication volume bound ranging from $O(dm\log(δ{n}))$ to $O(d^2m\log(δ^2n))$, depending on the receiver data set structure.

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arXiv2025

Optimal Communication Unbalanced Private Set Union

We present new two-party protocols for the Unbalanced Private Set Union (UPSU) problem. Here, the Sender holds a set of data points, and the Receiver holds another (possibly much larger) set, and they would like for the Receiver to learn the union of the two sets and nothing else. Furthermore, the Sender's computational cost, along with the communication complexity, should be smaller when the Sender has a smaller set. While the UPSU problem has numerous applications and has seen considerable recent attention in the literature, our protocols are the first where the Sender's computational cost and communication volume are linear in the size of the Sender's set only, and do not depend on the size of the Receiver's set. Our constructions combine linearly homomorphic encryption (LHE) with fully homomorphic encryption (FHE). The first construction uses multi-point polynomial evaluation (MEv) on FHE, and achieves optimal linear cost for the Sender, but has higher quadratic computational cost for the Receiver. In the second construction we explore another trade-off: the Receiver computes fast polynomial Euclidean remainder in FHE while the Sender computes a fast MEv, in LHE only. This reduces the Receiver's cost to quasi-linear, with a modest increase in the computational cost for the Sender. Preliminary experimental results using HElib indicate that, for example, a Sender holding 1000 elements can complete our first protocol using less than 2s of computation time and less than 10MB of communication volume, independently of the Receiver's set size.

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arXiv2025

Fast in-place accumulation

This paper deals with simultaneously fast and in-place algorithms for formulae where the result has to be linearly accumulated: some output variables are also input variables, linked by a linear dependency. Fundamental examples include the in-place accumulated multiplication of polynomials or matrices (that is with only $O(1)$ extra space). The difficulty is to combine in-place computations with fast algorithms: those usually come at the expense of (potentially large) extra temporary space. We first propose a novel automatic design of fast and in-place accumulating algorithms for any bilinear formulae (and thus for polynomial and matrix multiplication) and then extend it to any linear accumulation of a collection of functions. For this, we relax the in-place model to any algorithm allowed to modify its inputs, provided that those are restored to their initial state afterwards. This allows us to derive in-place accumulating algorithms for fast polynomial multiplications and for Strassen-like matrix multiplications. We then consider the simultaneously fast and in-place computation of the Euclidean polynomial remainder $R = A \bmod B$. If $A$ and $B$ have respective degree $m+n$ and $n$, and $M(k)$ denotes the complexity of a (not-in-place) algorithm to multiply two degree-$k$ polynomials, our algorithm uses at most $O((n/m) M(m)\log(m))$ arithmetic operations. If $M(n) = Θ(n^{1+ε})$ for some $ε>0$, then our algorithms do match the not-in-place complexity bound of $O((n/m) M(m))$. We also propose variants that compute - still in-place and with the same complexity bounds - $A = A \bmod B$, $R += A \bmod B$ and $R += AC \bmod B$, that is multiplication in a finite field extension. To achieve this, we develop techniques for Toeplitz matrix operations, for generalized convolutions, short product and power series division and remainder whose output is also part of the input.

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arXiv2025

Towards automated generation of fast and accurate algorithms for recursive matrix multiplication

We propose a strategy for the generation of fast and accurate versions of non-commutative recursive matrix multiplication algorithms. To generate these algorithms, we consider matrix and tensor norm bounds governing the stability and accuracy of numerical matrix multiplication. We start by a unification on known max-norm bounds on matrix multiplication stability and then extend them to further norms and more generally to recursive bilinear algorithms and the alternative basis matrix multiplication algorithms. Then our strategy has three phases. First, we reduce those bounds by minimizing a growth factor along the orbits of the associated matrix multiplication tensor decomposition. Second, we develop heuristics that minimize the number of operations required to realize a bilinear formula, while further improving its accuracy. Third, we perform an alternative basis sparsification that improves on the time complexity constant and mostly preserves the overall accuracy. For instance this strategy allows us to propose a non-commutative algorithm for multiplying 2x2-matrices using 7 coefficient products. This algorithm reaches simultaneously a better accuracy in practice compared to previously known such fast ___2x2x2:7___ Strassen-like algorithms and a time complexity bound with the best currently known leading term (obtained via alternative basis sparsification). We also present detailed results of our technique on other recursive matrix multiplication algorithms, such as Smirnov's ___3x3x6:40___ family of algorithms.

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arXiv2024

In-place accumulation of fast multiplication formulae

This paper deals with simultaneously fast and in-place algorithms for formulae where the result has to be linearly accumulated: some of the output variables are also input variables, linked by a linear dependency. Fundamental examples include the in-place accumulated multiplication of polynomials or matrices, C+=AB. The difficulty is to combine in-place computations with fast algorithms: those usually come at the expense of (potentially large) extra temporary space, but with accumulation the output variables are not even available to store intermediate values. We first propose a novel automatic design of fast and in-place accumulating algorithms for any bilinear formulae (and thus for polynomial and matrix multiplication) and then extend it to any linear accumulation of a collection of functions. For this, we relax the in-place model to any algorithm allowed to modify its inputs, provided that those are restored to their initial state afterwards. This allows us, in fine, to derive unprecedented in-place accumulating algorithms for fast polynomial multiplications and for Strassen-like matrix multiplications.

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arXiv2024

Strassen's algorithm is not optimally accurate

We propose a non-commutative algorithm for multiplying 2x2 matrices using 7 coefficient products. This algorithm reaches simultaneously a better accuracy in practice compared to previously known such fast algorithms, and a time complexity bound with the best currently known leading term (obtained via alternate basis sparsification). To build this algorithm, we consider matrix and tensor norms bounds governing the stability and accuracy of numerical matrix multiplication. First, we reduce those bounds by minimizing a growth factor along the unique orbit of Strassen's 2x2-matrix multiplication tensor decomposition. Second, we develop heuristics for minimizing the number of operations required to realize a given bilinear formula, while further improving its accuracy. Third, we perform an alternate basis sparsification that improves on the time complexity constant and mostly preserves the overall accuracy.

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arXiv2023

The forking effect

This study introduces the concept of the forking effect in the cryptocurrency market,specifically focusing on the impact of forking events on bitcoin, also called parent coin.We use a modified exponential GARCH model to examine the bitcoin's response inreturns and volatility. Our findings reveal that forking events do not significantlyaffect the bitcoin's returns but have a strong positive impact on its volatility, especially when considering market dynamics. Our model accounts for key features likevolatility clustering and fat-tailed distributions. Additionally, we observe that following a fork event, volatility remains elevated for the next three days, regardless ofother forking events, and the volatility impact does not increase when multiple forksoccur simultaneously on the same day.

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arXiv2023

VESPo: Verified Evaluation of Secret Polynomials

Proofs of Retrievability are protocols which allow a Client to store data remotely and to efficiently ensure, via audits, that the entirety of that data is still intact. Dynamic Proofs of Retrievability (DPoR) also support efficient retrieval and update of any small portion of the data.We propose a novel protocol for arbitrary outsourced data storage that achieves both low remote storage size and audit complexity.A key ingredient, that can be also of intrinsic interest, reduces to efficiently evaluating a secret polynomial at given public points, when the (encrypted) polynomial is stored on an untrusted Server.The Server performs the evaluations and also returns associated certificates. A Client can check that the evaluations are correct using the certificates and some pre-computed keys, more efficiently than re-evaluating the polynomial.Our protocols support two important features: the polynomial itself can be encrypted on the Server, and it can be dynamically updated by changing individual coefficients cheaply without redoing the entire setup.Our methods rely on linearly homomorphic encryption and pairings, and our implementation shows good performance for polynomial evaluations with millions of coefficients, and efficient DPoR with terabytes of data.For instance, for a 1TB database, compared to the state of art, we can reduce the Client storage by 5000x, communication size by 20x, and client-side audit time by 2x, at the cost of one order of magnitude increase in server-side audit time.

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arXiv2021

Dynamic proofs of retrievability with low server storage

Proofs of Retrievability (PoRs) are protocols which allow a client to store data remotely and to efficiently ensure, via audits, that the entirety of that data is still intact. A dynamic PoR system also supports efficient retrieval and update of any small portion of the data. We propose new, simple protocols for dynamic PoR that are designed for practical efficiency, trading decreased persistent storage for increased server computation, and show in fact that this tradeoff is inherent via a lower bound proof of time-space for any PoR scheme. Notably, ours is the first dynamic PoR which does not require any special encoding of the data stored on the server, meaning it can be trivially composed with any database service or with existing techniques for encryption or redundancy. Our implementation and deployment on Google Cloud Platform demonstrates our solution is scalable: for example, auditing a 1TB file takes just less than 5 minutes and costs less than $0.08 USD. We also present several further enhancements, reducing the amount of client storage, or the communication bandwidth, or allowing public verifiability, wherein any untrusted third party may conduct an audit.

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arXiv2021

Optimal Threshold Padlock Systems

In 1968, Liu described the problem of securing documents in a shared secret project. In an example, at least six out of eleven participating scientists need to be present to open the lock securing the secret documents. Shamir proposed a mathematical solution to this physical problem in 1979, by designing an efficient $k$-out-of-$n$ secret sharing scheme based on Lagrange's interpolation. Liu and Shamir also claimed that the minimal solution using physical locks is clearly impractical and exponential in the number of participants. In this paper we relax some implicit assumptions in their claim and propose an optimal physical solution to the problem of Liu that uses physical padlocks, but the number of padlocks is not greater than the number of participants. Then, we show that no device can do better for $k$-out-of-$n$ threshold padlock systems as soon as $k\geq{\sqrt{2n}}$, which holds true in particular for Liu's example. More generally, we derive bounds required to implement any threshold system and prove a lower bound of $\mathcal{O}{\log(n)}$ padlocks for any threshold larger than $2$. For instance we propose an optimal scheme reaching that bound for $2$-out-of-$n$ threshold systems and requiring less than $2\log_2(n)$ padlocks. We also discuss more complex access structures, a wrapping technique, and other sublinear realizations like an algorithm to generate $3$-out-of-$n$ systems with $2.5\sqrt{n}$ padlocks. Finally we give an algorithm building $k$-out-of-$n$ threshold padlock systems with only $\mathcal{O}{\log(n)^{k-1}}$ padlocks. Apart from the physical world, our results also show that it is possible to implement secret sharing over small fields.

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arXiv2021

Some fast algorithms multiplying a matrix by its adjoint

We present a non-commutative algorithm for the multiplication of a 2 x 2 block-matrix by its adjoint, defined by a matrix ring anti-homomorphism. This algorithm uses 5 block products (3 recursive calls and 2 general products)over C or in positive characteristic. The resulting algorithm for arbitrary dimensions is a reduction of multiplication of a matrix by its adjoint to general matrix product, improving by a constant factor previously known reductions. We prove also that there is no algorithm derived from bilinear forms using only four products and the adjoint of one of them. Second we give novel dedicated algorithms for the complex field and the quaternions to alternatively compute the multiplication taking advantage of the structure of the matrix-polynomial arithmetic involved. We then analyze the respective ranges of predominance of the two strategies. Finally we propose schedules with low memory footprint that support a fast and memory efficient practical implementation over a prime field.

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arXiv2020

On fast multiplication of a matrix by its transpose

We present a non-commutative algorithm for the multiplication of a 2x2-block-matrix by its transpose using 5 block products (3 recursive calls and 2 general products) over C or any finite field.We use geometric considerations on the space of bilinear forms describing 2x2 matrix products to obtain this algorithm and we show how to reduce the number of involved additions.The resulting algorithm for arbitrary dimensions is a reduction of multiplication of a matrix by its transpose to general matrix product, improving by a constant factor previously known reductions.Finally we propose schedules with low memory footprint that support a fast and memory efficient practical implementation over a finite field.To conclude, we show how to use our result in LDLT factorization.

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arXiv2020

A Faster Cryptographer's Conspiracy Santa

In Conspiracy Santa, a variant of Secret Santa, a group of people offer each other Christmas gifts, where each member of the group receives a gift from the other members of the group. To that end, the members of the group form conspiracies, to decide on appropriate gifts, and usually divide the cost of each gift among all participants of that conspiracy. This requires to settle the shared expenses per conspiracy, so Conspiracy Santa can actually be seen as an aggregation of several shared expenses problems. First, we show that the problem of finding a minimal number of transaction when settling shared expenses is NP-complete. Still, there exist good greedy approximations. Second, we present a greedy distributed secure solution to Conspiracy Santa. This solution allows a group of n people to share the expenses for the gifts in such a way that no participant learns the price of his gift, but at the same time notably reduces the number of transactions to 2 $\times$ n + 1 with respect to a na{ï}ve aggregation of n $\times$ (n -- 2). Furthermore, our solution does not require a trusted third party, and can either be implemented physically (the participants are in the same room and exchange money using envelopes) or, over Internet, using a cryptocurrency.

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