arXiv subjects

Friedrich Eisenbrand

Publications and source records attributed to Friedrich Eisenbrand.

At least 19 recordsLinked to original sources

arXiv2026

Polyhedral extended formulations that approximate the Gomory closure for packing problems

We consider $0/1$ packing problems $\max\{c^T x \colon Ax \leq 1, \, x \in \{0,1\}^n\}$, with $A \in \mathbb{R}_{\geq 0}^{m \times n}$. A way to solve such problems is via tightening the linear programming relaxation $P$ with Gomory \emph{cutting-planes}. The Gomory-closure $P'$ of $P$ is the intersection of $P$ with all its cutting planes. The optimization problem over $P'$ is NP-hard. Mastrolilli (2020) has shown that for fixed $ε>0$, the Lasserre hierarchy yields a polynomial-size convex but non-polyhedral extended formulation that approximates $P'$ up to a factor of $1+ε$. Our main result is the construction of a polyhedral and polynomial extended formulation that approximates $P'$ with the same approximation guarantee. Our construction is based on first principles. Like Mastrolilli's approach, ours also applies to higher iterates $P^{(t)}$ for fixed $t$ and $ε>0$. In contrast to an explicit construction, communication complexity provides an alternative way to describe extended formulations. Using this approach we obtain a quasi-polynomial polyhedral extended formulation for the above problem that is superior in some parameter regimes. To achieve this, we describe a communication protocol extending Yannakakis' protocol to decide whether the clique of Alice and the stable set of Bob intersect.

math.OC↗

arXiv2026

Nearly-Tight Bounds for Zonotope Containment and Beyond

We investigate the convex-body containment problem $\max\{s >0 : s Z \subseteq Q\}$, where the outer body $Q \subseteq \mathbb R^d$ is described by a membership oracle and the inner body $Z \subseteq \mathbb R^d$ is a zonotope. Our main result is a sampling-based $O(\sqrt{d})$-approximation algorithm for this problem that almost matches the lower bound of $Ω(\sqrt{d/\log d})$ by Khot and Naor in the oracle model. Assuming zonotopes can be sparsified by a linear number of generators, which is referred to as Talagrand conjecture, our approach attains the optimal approximation factor of $Θ(\sqrt{d/\log d})$. Our second main result is a proof of Talagrand's conjecture for $Δ$-modular zonotopes whenever $Δ$ is constant. Those zonotopes are of the form $Z = \{ Wx \colon \| x\|_\infty \leq 1\}$ where the non-zero $d \times d$ sub-determinants of $W$ are between $1$ and $Δ$. This result establishes a connection between zonoid sparsification and spectral sparsification of Batson, Spielman and Srivastava. We complement these results with a universal $Ω(\sqrt{d/\log d})$ lower bound holding for all zonotopes. Finally, we consider containment problems $\max\{s >0 : s K \subseteq Q\}$, for general convex bodies $K \subseteq \mathbb R^d$. A result of Naszódi on approximating $K \subseteq \mathbb R^d$ by a polytope implies a $Θ(d/\log d)$ approximation algorithm in polynomial time. We show the tightness of this approximation factor in the oracle model via a reduction to the circumradius computation. Our lower bound holds for centrally symmetric convex sets, implying that Barvinok's optimal $O(\sqrt{d})$-approximation of a centrally symmetric convex body by a polytope with a polynomial number of vertices cannot be computed in polynomial time.

cs.DS↗

arXiv2025

Excluding a Line Minor via Design Matrices and Column Number Bounds for the Circuit Imbalance Measure

For a real matrix $A \in \mathbb{R}^{d \times n}$ with non-collinear columns, we show that $n \leq O(d^4 κ_A)$ where $κ_A$ is the \emph{circuit imbalance measure} of $A$. The circuit imbalance measure $κ$ is a real analogue of $Δ$-modularity for integer matrices, satisfying $κ_A \leq Δ_A$ for integer $A$. The circuit imbalance measure has numerous applications in the context of linear programming (see Ekbatani, Natura and V{é}gh (2022) for a survey). Our result generalizes the $O(d^4 Δ_A)$ bound of Averkov and Schymura (2023) for integer matrices and provides the first polynomial bound holding for all parameter ranges on real matrices. To derive our result, similar to the strategy of Geelen, Nelson and Walsh (2021) for $Δ$-modular matrices, we show that real representable matroids induced by $κ$-bounded matrices are minor closed and exclude a rank $2$ uniform matroid on $O(κ)$ elements as a minor (also known as a line of length $O(κ)$). As our main technical contribution, we show that any simple rank $d$ complex representable matroid which excludes a line of length $l$ has at most $O(d^4 l)$ elements. This complements the tight bound of $(l-3)\binom{d}{2} + d$ for $l \geq 4$, of Geelen, Nelson and Walsh which holds when the rank $d$ is sufficiently large compared to $l$ (at least doubly exponential in $l$).

cs.DM↗

arXiv2025

A parameterized linear formulation of the integer hull

Let $A \in \mathbb{Z}^{m \times n}$ be an integer matrix with components bounded by $Δ$ in absolute value. Cook et al.~(1986) have shown that there exists a universal matrix $B \in \mathbb{Z}^{m' \times n}$ with the following property: For each $b \in \mathbb{Z}^m$, there exists $t \in \mathbb{Z}^{m'}$ such that the integer hull of the polyhedron $P = \{ x \in \mathbb{R}^n \colon Ax \leq b\}$ is described by $P_I = \{ x \in \mathbb{R}^n \colon Bx \leq t\}$. Our \emph{main result} is that $t$ is an \emph{affine} function of $b$ as long as $b$ is from a fixed equivalence class of the lattice $D \cdot \mathbb{Z}^m$. Here $D \in \mathbb{N}$ is a number that depends on $n$ and $Δ$ only. Furthermore, $D$ as well as the matrix $B$ can be computed in time depending on $Δ$ and $n$ only. An application of this result is the solution of an open problem posed by Cslovjecsek et al.~(SODA 2024) concerning the complexity of \emph{2-stage-stochastic integer programming} problems. The main tool of our proof is the classical theory of \emph{Chvátal-Gomory cutting planes} and the \emph{elementary closure} of rational polyhedra.

cs.CC↗

arXiv2025

Integer linear programming for unsupervised training set selection in molecular machine learning

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning applied to chemistry, we demonstrate the relevance of an ILP formulation to select molecular training sets for predictions of size-extensive properties. We show that our algorithm outperforms existing unsupervised training set selection approaches, especially when predicting properties of molecules larger than those present in the training set. We argue that the reason for the improved performance is due to the selection that is based on the notion of local similarity (i.e., per-atom) and a unique ILP approach that finds optimal solutions efficiently. Altogether, this work provides a practical algorithm to improve the performance of physics-inspired machine learning models and offers insights into the conceptual differences with existing training set selection approaches.

physics.chem-ph↗

arXiv2024

Sensitivity, Proximity and FPT Algorithms for Exact Matroid Problems

We consider the problem of finding a basis of a matroid with weight exactly equal to a given target. Here weights can be discrete values from $\{-Δ,\ldots,Δ\}$ or more generally $m$-dimensional vectors of such discrete values. We resolve the parameterized complexity completely, by presenting an FPT algorithm parameterized by $Δ$ and $m$ for arbitrary matroids. Prior to our work, no such algorithms were known even when weights are in $\{0,1\}$, or arbitrary $Δ$ and $m=1$. Our main technical contributions are new proximity and sensitivity bounds for matroid problems, independent of the number of elements. These bounds imply FPT algorithms via matroid intersection.

cs.DS↗

arXiv2024

Forall-exist statements in pseudopolynomial time

Given a convex set $Q \subseteq R^m$ and an integer matrix $W \in Z^{m \times n}$, we consider statements of the form $ \forall b \in Q \cap Z^m$ $\exists x \in Z^n$ s.t. $Wx \leq b$. Such statements can be verified in polynomial time with the algorithm of Kannan and its improvements if $n$ is fixed and $Q$ is a polyhedron. The running time of the best-known algorithms is doubly exponential in~$n$. In this paper, we provide a pseudopolynomial-time algorithm if $m$ is fixed. Its running time is $(m Δ)^{O(m^2)}$, where $Δ= \|W\|_\infty$. Furthermore it applies to general convex sets $Q$.

math.OC↗

arXiv2024

From approximate to exact integer programming

Approximate integer programming is the following: For a convex body $K \subseteq \mathbb{R}^n$, either determine whether $K \cap \mathbb{Z}^n$ is empty, or find an integer point in the convex body scaled by $2$ from its center of gravity $c$. Approximate integer programming can be solved in time $2^{O(n)}$ while the fastest known methods for exact integer programming run in time $2^{O(n)} \cdot n^n$. So far, there are no efficient methods for integer programming known that are based on approximate integer programming. Our main contribution are two such methods, each yielding novel complexity results. First, we show that an integer point $x^* \in (K \cap \mathbb{Z}^n)$ can be found in time $2^{O(n)}$, provided that the remainders of each component $x_i^* \mod{\ell}$ for some arbitrarily fixed $\ell \geq 5(n+1)$ of $x^*$ are given. The algorithm is based on a cutting-plane technique, iteratively halving the volume of the feasible set. The cutting planes are determined via approximate integer programming. Enumeration of the possible remainders gives a $2^{O(n)}n^n$ algorithm for general integer programming. This matches the current best bound of an algorithm by Dadush (2012) that is considerably more involved. Our algorithm also relies on a new asymmetric approximate Carathéodory theorem that might be of interest on its own. Our second method concerns integer programming problems in equation-standard form $Ax = b, 0 \leq x \leq u, \, x \in \mathbb{Z}^n$ . Such a problem can be reduced to the solution of $\prod_i O(\log u_i +1)$ approximate integer programming problems. This implies, for example that knapsack or subset-sum problems with polynomial variable range $0 \leq x_i \leq p(n)$ can be solved in time $(\log n)^{O(n)}$. For these problems, the best running time so far was $n^n \cdot 2^{O(n)}$.

math.OC↗

arXiv2023

An improved bound on sums of square roots via the subspace theorem

The sum of square roots is as follows: Given $x_1,\dots,x_n \in \mathbb{Z}$ and $a_1,\dots,a_n \in \mathbb{N}$ decide whether $ E=\sum_{i=1}^n x_i \sqrt{a_i} \geq 0$. It is a prominent open problem (Problem 33 of the Open Problems Project), whether this can be decided in polynomial time. The state-of-the-art methods rely on separation bounds, which are lower bounds on the minimum nonzero absolute value of $E$. The current best bound shows that $|E| \geq \left(n \cdot \max_i (|x_i| \cdot \sqrt{a_i})\right)^{-2^n} $, which is doubly exponentially small. We provide a new bound of the form $|E| \geq γ\cdot (n \cdot \max_i|x_i|)^{-2n}$ where $γ$ is a constant depending on $a_1,\dots,a_n$. This is singly exponential in $n$ for fixed $a_1,\dots,a_n$. The constant $γ$ is not explicit and stems from the subspace theorem, a deep result in the geometry of numbers.

cs.CG↗

arXiv2023

Integer points in the degree-sequence polytope

An integer vector $b \in \mathbb{Z}^d$ is a degree sequence if there exists a hypergraph with vertices $\{1,\dots,d\}$ such that each $b_i$ is the number of hyperedges containing $i$. The degree-sequence polytope $\mathscr{Z}^d$ is the convex hull of all degree sequences. We show that all but a $2^{-Ω(d)}$ fraction of integer vectors in the degree sequence polytope are degree sequences. Furthermore, the corresponding hypergraph of these points can be computed in time $2^{O(d)}$ via linear programming techniques. This is substantially faster than the $2^{O(d^2)}$ running time of the current-best algorithm for the degree-sequence problem. We also show that for $d\geq 98$, the degree-sequence polytope $\mathscr{Z}^d$ contains integer points that are not degree sequences. Furthermore, we prove that the linear optimization problem over $\mathscr{Z}^d$ is $\mathrm{NP}$-hard. This complements a recent result of Deza et al. (2018) who provide an algorithm that is polynomial in $d$ and the number of hyperedges.

cs.DM↗

arXiv2022

An Algorithmic Theory of Integer Programming

We study the general integer programming problem where the number of variables $n$ is a variable part of the input. We consider two natural parameters of the constraint matrix $A$: its numeric measure $a$ and its sparsity measure $d$. We show that integer programming can be solved in time $g(a,d)\textrm{poly}(n,L)$, where $g$ is some computable function of the parameters $a$ and $d$, and $L$ is the binary encoding length of the input. In particular, integer programming is fixed-parameter tractable parameterized by $a$ and $d$, and is solvable in polynomial time for every fixed $a$ and $d$. Our results also extend to nonlinear separable convex objective functions. Moreover, for linear objectives, we derive a strongly-polynomial algorithm, that is, with running time $g(a,d)\textrm{poly}(n)$, independent of the rest of the input data. We obtain these results by developing an algorithmic framework based on the idea of iterative augmentation: starting from an initial feasible solution, we show how to quickly find augmenting steps which rapidly converge to an optimum. A central notion in this framework is the Graver basis of the matrix $A$, which constitutes a set of fundamental augmenting steps. The iterative augmentation idea is then enhanced via the use of other techniques such as new and improved bounds on the Graver basis, rapid solution of integer programs with bounded variables, proximity theorems and a new proximity-scaling algorithm, the notion of a reduced objective function, and others. As a consequence of our work, we advance the state of the art of solving block-structured integer programs. In particular, we develop near-linear time algorithms for $n$-fold, tree-fold, and $2$-stage stochastic integer programs. We also discuss some of the many applications of these classes.

math.OC↗

arXiv2021

A QPTAS for stabbing rectangles

We consider the following geometric optimization problem: Given $ n $ axis-aligned rectangles in the plane, the goal is to find a set of horizontal segments of minimum total length such that each rectangle is stabbed. A segment stabs a rectangle if it intersects both its left and right edge. As such, this stabbing problem falls into the category of weighted geometric set cover problems for which techniques that improve upon the general $Θ(\log n)$-approximation guarantee have received a lot of attention in the literature. Chan at al. (2018) have shown that rectangle stabbing is NP-hard and that it admits a constant-factor approximation algorithm based on Varadarajan's quasi-uniform sampling method. In this work we make progress on rectangle stabbing on two fronts. First, we present a quasi-polynomial time approximation scheme (QPTAS) for rectangle stabbing. Furthermore, we provide a simple $8$-approximation algorithm that avoids the framework of Varadarajan. This settles two open problems raised by Chan et al. (2018).

cs.CG↗

arXiv2020

Efficient sequential and parallel algorithms for multistage stochastic integer programming using proximity

We consider the problem of solving integer programs of the form $\min \{\,c^\intercal x\ \colon\ Ax=b, x\geq 0\}$, where $A$ is a multistage stochastic matrix in the following sense: the primal treedepth of $A$ is bounded by a parameter $d$, which means that the columns of $A$ can be organized into a rooted forest of depth at most $d$ so that columns not bound by the ancestor/descendant relation in the forest do not have non-zero entries in the same row. We give an algorithm that solves this problem in fixed-parameter time $f(d,\|A\|_{\infty})\cdot n\log^{O(2^d)} n$, where $f$ is a computable function and $n$ is the number of rows of $A$. The algorithm works in the strong model, where the running time only measures unit arithmetic operations on the input numbers and does not depend on their bitlength. This is the first fpt algorithm for multistage stochastic integer programming to achieve almost linear running time in the strong sense. For the case of two-stage stochastic integer programs, our algorithm works in time $2^{(2\|A\|_\infty)^{O(r(r+s))}}\cdot n\log^{O(rs)} n$. The algorithm can be also parallelized: we give an implementation in the PRAM model that achieves running time $f(d,\|A\|_{\infty})\cdot \log^{O(2^d)} n$ using $n$ processors. The main conceptual ingredient in our algorithms is a new proximity result for multistage stochastic integer programs. We prove that if we consider an integer program $P$, say with a constraint matrix $A$, then for every optimum solution to the linear relaxation of $P$ there exists an optimum (integral) solution to $P$ that lies, in the $\ell_{\infty}$-norm, within distance bounded by a function of $\|A\|_{\infty}$ and the primal treedepth of $A$. On the way to achieve this result, we prove a generalization and considerable improvement of a structural result of Klein for multistage stochastic integer programs.

cs.DS↗

arXiv2020

Block-Structured Integer and Linear Programming in Strongly Polynomial and Near Linear Time

We consider integer and linear programming problems for which the linear constraints exhibit a (recursive) block-structure: The problem decomposes into independent and efficiently solvable sub-problems if a small number of constraints is deleted. A prominent example are $n$-fold integer programming problems and their generalizations which have received considerable attention in the recent literature. The previously known algorithms for these problems are based on the augmentation framework, a tailored integer programming variant of local search. In this paper we propose a different approach. Our algorithm relies on parametric search and a new proximity bound. We show that block-structured linear programming can be solved efficiently via an adaptation of a parametric search framework by Norton, Plotkin, and Tardos in combination with Megiddo's multidimensional search technique. This also forms a subroutine of our algorithm for the integer programming case by solving a strong relaxation of it. Then we show that, for any given optimal vertex solution of this relaxation, there is an optimal integer solution within $\ell_1$-distance independent of the dimension of the problem. This in turn allows us to find an optimal integer solution efficiently. We apply our techniques to integer and linear programming with $n$-fold structure or bounded dual treedepth, two benchmark problems in this field. We obtain the first algorithms for these cases that are both near-linear in the dimension of the problem and strongly polynomial. Moreover, unlike the augmentation algorithms, our approach is highly parallelizable.

cs.CC↗

arXiv2020

Approximate $\mathrm{CVP}_{p}$ in time $2^{0.802 \, n}$

We show that a constant factor approximation of the shortest and closest lattice vector problem w.r.t. any $\ell_p$-norm can be computed in time $2^{(0.802 +ε)\, n}$. This matches the currently fastest constant factor approximation algorithm for the shortest vector problem w.r.t. $\ell_2$. To obtain our result, we combine the latter algorithm w.r.t. $\ell_2$ with geometric insights related to coverings.

cs.CG↗

arXiv2019

Proximity results and faster algorithms for Integer Programming using the Steinitz Lemma

We consider integer programming problems in standard form $\max \{c^Tx : Ax = b, \, x\geq 0, \, x \in Z^n\}$ where $A \in Z^{m \times n}$, $b \in Z^m$ and $c \in Z^n$. We show that such an integer program can be solved in time $(m Δ)^{O(m)} \cdot \|b\|_\infty^2$, where $Δ$ is an upper bound on each absolute value of an entry in $A$. This improves upon the longstanding best bound of Papadimitriou (1981) of $(m\cdot Δ)^{O(m^2)}$, where in addition, the absolute values of the entries of $b$ also need to be bounded by $Δ$. Our result relies on a lemma of Steinitz that states that a set of vectors in $R^m$ that is contained in the unit ball of a norm and that sum up to zero can be ordered such that all partial sums are of norm bounded by $m$. We also use the Steinitz lemma to show that the $\ell_1$-distance of an optimal integer and fractional solution, also under the presence of upper bounds on the variables, is bounded by $m \cdot (2\,m \cdot Δ+1)^m$. Here $Δ$ is again an upper bound on the absolute values of the entries of $A$. The novel strength of our bound is that it is independent of $n$. We provide evidence for the significance of our bound by applying it to general knapsack problems where we obtain structural and algorithmic results that improve upon the recent literature.

cs.DM↗

arXiv2018

Diversity maximization in doubling metrics

Diversity maximization is an important geometric optimization problem with many applications in recommender systems, machine learning or search engines among others. A typical diversification problem is as follows: Given a finite metric space $(X,d)$ and a parameter $k \in \mathbb{N}$, find a subset of $k$ elements of $X$ that has maximum diversity. There are many functions that measure diversity. One of the most popular measures, called remote-clique, is the sum of the pairwise distances of the chosen elements. In this paper, we present novel results on three widely used diversity measures: Remote-clique, remote-star and remote-bipartition. Our main result are polynomial time approximation schemes for these three diversification problems under the assumption that the metric space is doubling. This setting has been discussed in the recent literature. The existence of such a PTAS however was left open. Our results also hold in the setting where the distances are raised to a fixed power $q\geq 1$, giving rise to more variants of diversity functions, similar in spirit to the variations of clustering problems depending on the power applied to the distances. Finally, we provide a proof of NP-hardness for remote-clique with squared distances in doubling metric spaces.

cs.DM↗

arXiv2018

Faster Algorithms for Integer Programs with Block Structure

We consider integer programming problems $\max \{ c^T x : \mathcal{A} x = b, l \leq x \leq u, x \in \mathbb{Z}^{nt}\}$ where $\mathcal{A}$ has a (recursive) block-structure generalizing "$n$-fold integer programs" which recently received considerable attention in the literature. An $n$-fold IP is an integer program where $\mathcal{A}$ consists of $n$ repetitions of submatrices $A \in \mathbb{Z}^{r \times t}$ on the top horizontal part and $n$ repetitions of a matrix $B \in \mathbb{Z}^{s \times t}$ on the diagonal below the top part. Instead of allowing only two types of block matrices, one for the horizontal line and one for the diagonal, we generalize the $n$-fold setting to allow for arbitrary matrices in every block. We show that such an integer program can be solved in time $n^2 t^2 ϕ \cdot (rsΔ)^{\mathcal{O}(rs^2+ sr^2)}$ (ignoring logarithmic factors). Here $Δ$ is an upper bound on the largest absolute value of an entry of $\mathcal{A}$ and $ϕ$ is the largest binary encoding length of a coefficient of $c$. This improves upon the previously best algorithm of Hemmecke, Onn and Romanchuk that runs in time $n^3t^3 ϕ \cdot Δ^{\mathcal{O}(t^2s)}$. In particular, our algorithm is not exponential in the number $t$ of columns of $A$ and $B$. Our algorithm is based on a new upper bound on the $l_1$-norm of an element of the "Graver basis" of an integer matrix and on a proximity bound between the LP and IP optimal solutions tailored for IPs with block structure. These new bounds rely on the "Steinitz Lemma". Furthermore, we extend our techniques to the recently introduced "tree-fold IPs", where we again present a more efficient algorithm in a generalized setting.

cs.DM↗