arXiv subjects

Hiroshi Hirai

Publications and source records attributed to Hiroshi Hirai.

At least 19 recordsLinked to original sources

arXiv2026

On integral polytopes related to Edmonds' problem

In this paper, we study polyhedral aspects on commutative and noncommutative Edmonds' problems for computing the rank of linear symbolic matrix $A = \sum_{k=1}^m A_k x_k$. We regard them as linear optimization over integral polytopes ${\cal P}(A)$ and ${\cal Q}(A)$, respectively, which are obtained by the convex hulls of exponent vectors of subdeterminants of~$A$ and its blow-ups $A^{\{d\}} = \sum_{k=1}^m A_k \otimes X_k$ $(d=1,2,\ldots)$. By extending previously known results on nc-rank, we establish a hierarchy of integral polytopes ${\cal P}(A) \subseteq {\cal P}^{\leq 2}(A) \subseteq {\cal P}^{\leq 3}(A) \subseteq \cdots = {\cal Q}(A)$ and show that the integrality gap of ${\cal Q}(A)$ relative to ${\cal P}(A)$ is at least $1/2$. Further, we show that if each $A_k$ is rank-2 skew-symmetric, then the above hierarchy terminates at the second level and the integrality gap is improved to $2/3$.

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arXiv2026

Horospherically convex optimization for fractional subspace packing and its applications

In this paper, we address a semi-infinite LP relaxation of the vector-subspace packing problem. This is a higher-dimensional generalization of the fractional linear matroid parity problem and is closely related to Brascamp-Lieb polytopes. We show that the dual of this LP can be formulated as ``linear programming on a Euclidean building," namely, the problem of minimizing a Busemann function over an intersection of horoballs. This provides a natural example of horospherically convex optimization, recently introduced by Goodwin et al. (2026) and Criscitiello and Kim (2025). By applying the incremental Busemann subgradient method, we obtain an additive FPTAS for the problem. As applications, we obtain a new and simpler polynomial-time algorithm for fractional linear matroid parity, and new algorithms for the membership problem of Brascamp-Lieb polytopes.

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arXiv2026

Gradient descent for unbounded convex functions on Hadamard manifolds and its applications to scaling problems

In this paper, we study the asymptotic behavior of continuous- and discrete-time gradient flows of a ``lower-unbounded" convex function $f$ on a Hadamard manifold $M$, particularly, their convergence properties to the boundary $M^{\infty}$ at infinity of $M$. We establish a duality theorem that the infimum of the gradient-norm $\|\nabla f(x)\|$ of $f$ over $M$ is equal to the supremum of the negative of the recession function $f^{\infty}$ of $f$ over the boundary $M^{\infty}$, provided the infimum is positive. Further, the infimum and the supremum are obtained by the limit of the gradient flow of $f$. Our results feature convex-optimization ingredients of the moment-weight inequality for reductive group actions by Georgoulas, Robbin, and Salamon, and are applied to noncommutative optimization by Bürgisser et al. FOCS 2019. We show that gradient descent of the Kempf-Ness function for an unstable orbit converges to a destabilizing 1-parameter subgroup in the Hilbert-Mumford criterion, and the associated moment-map sequence converges to the minimum-norm point of the moment polytope. We show further refinements for operator scaling -- the left-right action on a matrix tuple $A= (A_1,A_2,\ldots,A_N)$. We characterize the gradient-flow limit of operator scaling by a vector-space generalization of the classical Dulmage-Mendelsohn decomposition of a bipartite graph. For a special case of $N = 2$, we reveal that the limit determines the Kronecker canonical form of a matrix pencil $s A_1+A_2$.

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arXiv2026

Generalized gradient flows in Hadamard manifolds and convex optimization on entanglement polytopes

In this paper, we address the optimization problem of minimizing $Q(df_x)$ over a Hadamard manifold ${\cal M}$, where $f$ is a convex function on ${\cal M}$, $df_x$ is the differential of $f$ at $x \in {\cal M}$, and $Q$ is a function on the cotangent bundle of ${\cal M}$. This problem generalizes the problem of minimizing the gradient norm $\|\nabla f(x)\|$ over ${\cal M}$, studied by Hirai and Sakabe FOCS2024. We formulate a natural class of $Q$ in terms of convexity and invariance under parallel transports, and introduce a generalization of the gradient flow of $f$ that is expected to minimize $Q(df_x)$. For basic classes of manifolds, including the product of the manifolds of positive definite matrices, we prove that this gradient flow attains $\inf_{x\in {\cal M}} Q(df_x)$ in the limit, and yields a duality relation. This result is applied to the Kempf-Ness optimization for GL-actions on tensors, which is Euclidean convex optimization on the class of moment polytopes, known as the entanglement polytopes. This type of convex optimization arises from tensor-related subjects in theoretical computer science, such as quantum functional, $G$-stable rank, and noncommutative rank.

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arXiv2025

A scaling characterization of nc-rank via unbounded gradient flow

Given a tuple of $n \times n$ complex matrices ${\cal A} = (A_1,A_2,\ldots, A_m)$, the linear symbolic matrix $A = A_1x_1 + A_2x_2 + \cdots + A_m x_m$ is nonsingular in the noncommutative sense if and only if the completely positive operators $T_{\cal A} (X) = \sum_{i=1}^m A_i X A_i^{\dagger}$ and $T_{\cal A}^*(X) = \sum_{i=1}^m A_i^{\dagger} X A_i$ can be scaled to be doubly stochastic: For every $ε> 0$ there are $g,h \in GL(n,\mathbb{C})$ such that $\|T_{g^{\dagger}{\cal A}h}(I)- I\| < ε$, $\| T^*_{g^\dagger{\cal A}h}(I) - I\| < ε$. In this paper, we show a refinement: The noncommutative corank of $A$ is equal to one-half of the minimum residual $\|T_{g^{\dagger}{\cal A}h}(I) - I\|_1 + \|T^*_{g^{\dagger}{\cal A}h}(I) - I\|_1$ over all possible scalings $g^{\dagger}{\cal A}h$, where $\|\cdot \|_1$ is the trace norm. To show this, we interpret the residuals as gradients of a convex function on symmetric space $GL(n,\mathbb{C})/U_n$, and establish a general duality relation of the minimum gradient-norm of a lower-unbounded convex function $f$ on $GL(n,\mathbb{C})/U_n$ with an invariant Finsler metric, by utilizing the unbounded gradient flow of $f$ at infinity.

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arXiv2024

Polyhedral Clinching Auctions for Indivisible Goods

In this study, we propose the polyhedral clinching auction for indivisible goods, which has so far been studied for divisible goods. As in the divisible setting by Goel et al. (2015), our mechanism enjoys incentive compatibility, individual rationality, and Pareto optimality, and works with polymatroidal environments. A notable feature for the indivisible setting is that the whole procedure can be conducted in time polynomial of the number of buyers and goods. Moreover, we show additional efficiency guarantees, recently established by Sato for the divisible setting: The liquid welfare (LW) of our mechanism achieves more than 1/2 of the optimal LW, and that the social welfare is more than the optimal LW.

cs.GT↗

arXiv2024

Minimum 0-Extension Problems on Directed Metrics

For a metric $μ$ on a finite set $T$, the minimum 0-extension problem 0-Ext$[μ]$ is defined as follows: Given $V\supseteq T$ and $\ c:{V \choose 2}\rightarrow \mathbf{Q_+}$, minimize $\sum c(xy)μ(γ(x),γ(y))$ subject to $γ:V\rightarrow T,\ γ(t)=t\ (\forall t\in T)$, where the sum is taken over all unordered pairs in $V$. This problem generalizes several classical combinatorial optimization problems such as the minimum cut problem or the multiterminal cut problem. Karzanov and Hirai established a complete classification of metrics $μ$ for which 0-Ext$[μ]$ is polynomial time solvable or NP-hard. This result can also be viewed as a sharpening of the general dichotomy theorem for finite-valued CSPs (Thapper and Živný 2016) specialized to 0-Ext$[μ]$. In this paper, we consider a directed version $\overrightarrow{0}$-Ext$[μ]$ of the minimum 0-extension problem, where $μ$ and $c$ are not assumed to be symmetric. We extend the NP-hardness condition of 0-Ext$[μ]$ to $\overrightarrow{0}$-Ext$[μ]$: If $μ$ cannot be represented as the shortest path metric of an orientable modular graph with an orbit-invariant ``directed'' edge-length, then $\overrightarrow{0}$-Ext$[μ]$ is NP-hard. We also show a partial converse: If $μ$ is a directed metric of a modular lattice with an orbit-invariant directed edge-length, then $\overrightarrow{0}$-Ext$[μ]$ is tractable. We further provide a new NP-hardness condition characteristic of $\overrightarrow{0}$-Ext$[μ]$, and establish a dichotomy for the case where $μ$ is a directed metric of a star.

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arXiv2023

Algebraic combinatorial optimization on the degree of determinants of noncommutative symbolic matrices

We address the computation of the degrees of minors of a noncommutative symbolic matrix of form \[ A[c] := \sum_{k=1}^m A_k t^{c_k} x_k, \] where $A_k$ are matrices over a field $\mathbb{K}$, $x_i$ are noncommutative variables, $c_k$ are integer weights, and $t$ is a commuting variable specifying the degree. This problem extends noncommutative Edmonds' problem (Ivanyos et al. 2017), and can formulate various combinatorial optimization problems. Extending the study by Hirai 2018, and Hirai, Ikeda 2022, we provide novel duality theorems and polyhedral characterization for the maximum degrees of minors of $A[c]$ of all sizes, and develop a strongly polynomial-time algorithm for computing them. This algorithm is viewed as a unified algebraization of the classical Hungarian method for bipartite matching and the weight-splitting algorithm for linear matroid intersection. As applications, we provide polynomial-time algorithms for weighted fractional linear matroid matching and linear optimization over rank-2 Brascamp-Lieb polytopes.

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arXiv2023

Helly groups

Helly graphs are graphs in which every family of pairwise intersecting balls has a non-empty intersection. This is a classical and widely studied class of graphs. In this article we focus on groups acting geometrically on Helly graphs -- Helly groups. We provide numerous examples of such groups: all (Gromov) hyperbolic, CAT(0) cubical, finitely presented graphical C(4)$-$T(4) small cancellation groups, and type-preserving uniform lattices in Euclidean buildings of type $C_n$ are Helly; free products of Helly groups with amalgamation over finite subgroups, graph products of Helly groups, some diagram products of Helly groups, some right-angled graphs of Helly groups, and quotients of Helly groups by finite normal subgroups are Helly. We show many properties of Helly groups: biautomaticity, existence of finite dimensional models for classifying spaces for proper actions, contractibility of asymptotic cones, existence of EZ-boundaries, satisfiability of the Farrell-Jones conjecture and of the coarse Baum-Connes conjecture. This leads to new results for some classical families of groups (e.g. for FC-type Artin groups) and to a unified approach to results obtained earlier.

math.GR↗

arXiv2023

Finding Hall blockers by matrix scaling

For a given nonnegative matrix $A=(A_{ij})$, the matrix scaling problem asks whether $A$ can be scaled to a doubly stochastic matrix $D_1AD_2$ for some positive diagonal matrices $D_1,D_2$.The Sinkhorn algorithm is a simple iterative algorithm, which repeats row-normalization $A_{ij} \leftarrow A_{ij}/\sum_{j}A_{ij}$ and column-normalization $A_{ij} \leftarrow A_{ij}/\sum_{i}A_{ij}$ alternatively. By this algorithm, $A$ converges to a doubly stochastic matrix in limit if and only if the bipartite graph associated with $A$ has a perfect matching. This property can decide the existence of a perfect matching in a given bipartite graph $G$, which is identified with the $0,1$-matrix $A_G$.Linial, Samorodnitsky, and Wigderson showed that $O(n^2 \log n)$ iterations for $A_G$ decide whether $G$ has a perfect matching. Here $n$ is the number of vertices in one of the color classes of $G$. In this paper, we show an extension of this result:If $G$ has no perfect matching, then a polynomial number of the Sinkhorn iterations identifies a Hall blocker -- a vertex subset $X$ having neighbors $Γ(X)$ with $|X| > |Γ(X)|$. Specifically, we show that $O(n^2 \log n)$ iterations can identify one Hall blocker, and that further polynomial iterations can also identify all parametric Hall blockers $X$ of maximizing $(1-λ) |X| - λ|Γ(X)|$ for $λ\in [0,1]$.The former result is based on an interpretation of the Sinkhorn algorithm as alternating minimization for geometric programming. The latter is on an interpretation as alternating minimization for KL-divergence (Csiszár and Tusnády 1984, Gietl and Reffel 2013) and its limiting behavior for a nonscalable matrix (Aas 2014). We also relate the Sinkhorn limit with parametric network flow, principal partition of polymatroids, and the Dulmage-Mendelsohn decomposition of a bipartite graph.

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arXiv2023

Convex analysis on Hadamard spaces and scaling problems

In this paper, we address the bounded/unbounded determination of geodesically convex optimization on Hadamard spaces. In Euclidean convex optimization, the recession function is a basic tool to study the unboundedness, and provides the domain of the Legendre-Fenchel conjugate of the objective function. In a Hadamard space, the asymptotic slope function (Kapovich, Leeb, and Millson 2009), which is a function on the boundary at infinity, plays a role of the recession function. We extend this notion by means of convex analysis and optimization, and develop a convex analysis foundation for the unbounded determination of geodesically convex optimization on Hadamard spaces, particularly on symmetric spaces of nonpositive curvature. We explain how our developed theory is applied to operator scaling and related optimization on group orbits, which are our motivation.

math.OC↗

arXiv2023

On a manifold formulation of self-concordant functions

In this paper, we address an extension of the theory of self-concordant functions for a manifold. We formulate the self-concordance of a geodesically convex function by a condition of the covariant derivative of its Hessian, and verify that many of the analogous properties, such as the quadratic convergence of Newton's method and the polynomial iteration complexity of the path-following method, are naturally extended. However it is not known whether a useful class of self-concordant functions/barriers really exists for non-Euclidean manifolds. To this question, we provide a preliminary result that the squared distance function in the hyperbolic space of curvature $- κ$ is $\sqrtκ/2$-self-concordant and the associated logarithmic barrier of a ball of radius $R$ is an $O(κR^2)$-self-concordant barrier. We also give an application to the minimum enclosing ball in a hyperbolic space.

math.OC↗

arXiv2023

Interior-point methods on manifolds: theory and applications

Interior-point methods offer a highly versatile framework for convex optimization that is effective in theory and practice. A key notion in their theory is that of a self-concordant barrier. We give a suitable generalization of self-concordance to Riemannian manifolds and show that it gives the same structural results and guarantees as in the Euclidean setting, in particular local quadratic convergence of Newton's method. We analyze a path-following method for optimizing compatible objectives over a convex domain for which one has a self-concordant barrier, and obtain the standard complexity guarantees as in the Euclidean setting. We provide general constructions of barriers, and show that on the space of positive-definite matrices and other symmetric spaces, the squared distance to a point is self-concordant. To demonstrate the versatility of our framework, we give algorithms with state-of-the-art complexity guarantees for the general class of scaling and non-commutative optimization problems, which have been of much recent interest, and we provide the first algorithms for efficiently finding high-precision solutions for computing minimal enclosing balls and geometric medians in nonpositive curvature.

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arXiv2022

Two flags in a semimodular lattice generate an antimatroid

A basic property in a modular lattice is that any two flags generate a distributive sublattice. It is shown (Abels 1991, Herscovic 1998) that two flags in a semimodular lattice no longer generate such a good sublattice, whereas shortest galleries connecting them form a relatively good join-sublattice. In this note, we sharpen this investigation to establish an analogue of the two-flag generation theorem for a semimodular lattice. We consider the notion of a modular convex subset, which is a subset closed under the join and meet only for modular pairs, and show that the modular convex hull of two flags in a semimodular lattice of rank $n$ is isomorphic to a union-closed family on $[n]$. This family uniquely determines an antimatroid, which coincides with the join-sublattice of shortest galleries of the two flags.

math.CO↗

arXiv2022

Reconstructing phylogenetic trees from multipartite quartet systems

A phylogenetic tree is a graphical representation of an evolutionary history of taxa in which the leaves correspond to the taxa and the non-leaves correspond to speciations. One of important problems in phylogenetic analysis is to assemble a global phylogenetic tree from small phylogenetic trees, particularly, quartet trees. {\sc Quartet Compatibility} is the problem of deciding whether there is a phylogenetic tree inducing a given collection of quartet trees, and to construct such a phylogenetic tree if it exists. It is known that {\sc Quartet Compatibility} is NP-hard and that there are only a few results known for polynomial-time solvable subclasses. In this paper, we introduce two novel classes of quartet systems, called complete multipartite quartet system and full multipartite quartet system, and present polynomial-time algorithms for {\sc Quartet Compatibility} for these systems.

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arXiv2021

A combinatorial algorithm for computing the rank of a generic partitioned matrix with $2 \times 2$ submatrices

In this paper, we consider the problem of computing the rank of a block-structured symbolic matrix (a generic partitioned matrix) $A = (A_{αβ} x_{αβ})$, where $A_{αβ}$ is a $2 \times 2$ matrix over a field $\mathbf{F}$ and $x_{αβ}$ is an indeterminate for $α= 1,2,\dots, μ$ and $β= 1,2, \dots, ν$. This problem can be viewed as an algebraic generalization of the bipartite matching problem and was considered by Iwata and Murota (1995). Recent interests in this problem lie in the connection with non-commutative Edmonds' problem by Ivanyos, Qiao, and Subrahamanyam (2018) and Garg, Gurvits, Oliveiva, and Wigderson (2019), where a result by Iwata and Murota implicitly states that the rank and non-commutative rank (nc-rank) are the same for this class of symbolic matrices. The main result of this paper is a simple and combinatorial $O((μν)^2 \min \{ μ, ν\})$-time algorithm for computing the symbolic rank of a $(2 \times 2)$-type generic partitioned matrix of size $2μ\times 2ν$. Our algorithm is inspired by the Wong sequence algorithm by Ivanyos, Qiao, and Subrahamanyam for the nc-rank of a general symbolic matrix, and requires no blow-up operation, no field extension, and no additional care for bounding the bit-size. Moreover it naturally provides a maximum rank completion of $A$ for an arbitrary field $\mathbf{F}$.

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arXiv2020

Computing the nc-rank via discrete convex optimization on CAT(0) spaces

In this paper, we address the noncommutative rank (nc-rank) computation of a linear symbolic matrix \[ A = A_1 x_1 + A_2 x_2 + \cdots + A_m x_m, \] where each $A_i$ is an $n \times n$ matrix over a field $\mathbb{K}$, and $x_i$ $(i=1,2,\ldots,m)$ are noncommutative variables. For this problem, polynomial time algorithms were given by Garg, Gurvits, Oliveira, and Wigderson for $\mathbb{K} = \mathbb{Q}$, and by Ivanyos, Qiao, and Subrahmanyam for an arbitrary field $\mathbb{K}$. We present a significantly different polynomial time algorithm that works on an arbitrary field $\mathbb{K}$. Our algorithm is based on a combination of submodular optimization on modular lattices and convex optimization on CAT(0) spaces.

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arXiv2020

A cost-scaling algorithm for computing the degree of determinants

In this paper, we address computation of the degree $\mathop{\rm deg Det} A$ of Dieudonné determinant $\mathop{\rm Det} A$ of \[ A = \sum_{k=1}^m A_k x_k t^{c_k}, \] where $A_k$ are $n \times n$ matrices over a field $\mathbb{K}$, $x_k$ are noncommutative variables, $t$ is a variable commuting with $x_k$, $c_k$ are integers, and the degree is considered for $t$. This problem generalizes noncommutative Edmonds' problem and fundamental combinatorial optimization problems including the weighted linear matroid intersection problem. It was shown that $\mathop{\rm deg Det} A$ is obtained by a discrete convex optimization on a Euclidean building. We extend this framework by incorporating a cost scaling technique, and show that $\mathop{\rm deg Det} A$ can be computed in time polynomial of $n,m,\log_2 C$, where $C:= \max_k |c_k|$. We give a polyhedral interpretation of $\mathop{\rm deg Det}$, which says that $\mathop{\rm deg Det} A$ is given by linear optimization over an integral polytope with respect to objective vector $c = (c_k)$. Based on it, we show that our algorithm becomes a strongly polynomial one. We apply this result to an algebraic combinatorial optimization problem arising from a symbolic matrix having $2 \times 2$-submatrix structure.

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