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1. A framework for quadratic form maximization over convex sets through nonconvex relaxations

   https://doi.org/10.1145/3406325.3451128  

   Bhattiprolu, Vijay ; Lee, Euiwoong ; Naor, Assaf ( January 2021 , STOC'21)

   null (Ed.)

   We investigate the approximability of the following optimization problem. The input is an n× n matrix A=(Aij) with real entries and an origin-symmetric convex body K⊂ ℝn that is given by a membership oracle. The task is to compute (or approximate) the maximum of the quadratic form ∑i=1n∑j=1n Aij xixj=⟨ x,Ax⟩ as x ranges over K. This is a rich and expressive family of optimization problems; for different choices of matrices A and convex bodies K it includes a diverse range of optimization problems like max-cut, Grothendieck/non-commutative Grothendieck inequalities, small set expansion and more. While the literature studied these special cases using case-specific reasoning, here we develop a general methodology for treatment of the approximability and inapproximability aspects of these questions. The underlying geometry of K plays a critical role; we show under commonly used complexity assumptions that polytime constant-approximability necessitates that K has type-2 constant that grows slowly with n. However, we show that even when the type-2 constant is bounded, this problem sometimes exhibits strong hardness of approximation. Thus, even within the realm of type-2 bodies, the approximability landscape is nuanced and subtle. However, the link that we establish between optimization and geometry of Banach spaces allows us to devise a generic algorithmic approach to the above problem. We associate to each convex body a new (higher dimensional) auxiliary set that is not convex, but is approximately convex when K has a bounded type-2 constant. If our auxiliary set has an approximate separation oracle, then we design an approximation algorithm for the original quadratic optimization problem, using an approximate version of the ellipsoid method. Even though our hardness result implies that such an oracle does not exist in general, this new question can be solved in specific cases of interest by implementing a range of classical tools from functional analysis, most notably the deep factorization theory of linear operators. Beyond encompassing the scenarios in the literature for which constant-factor approximation algorithms were found, our generic framework implies that that for convex sets with bounded type-2 constant, constant factor approximability is preserved under the following basic operations: (a) Subspaces, (b) Quotients, (c) Minkowski Sums, (d) Complex Interpolation. This yields a rich family of new examples where constant factor approximations are possible, which were beyond the reach of previous methods. We also show (under commonly used complexity assumptions) that for symmetric norms and unitarily invariant matrix norms the type-2 constant nearly characterizes the approximability of quadratic maximization. 

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2. Exclusion volumes of convex bodies in high space dimensions: applications to virial coefficients and continuum percolation

   https://doi.org/10.1088/1742-5468/ac8c8b  

   Torquato, Salvatore ; Jiao, Yang ( September 2022 , Journal of Statistical Mechanics: Theory and Experiment)

   Abstract Using the concepts of mixed volumes and quermassintegrals of convex geometry, we derive an exact formula for the exclusion volume v ex ( K ) for a general convex body K that applies in any space dimension. While our main interests concern the rotationally-averaged exclusion volume of a convex body with respect to another convex body, we also describe some results for the exclusion volumes for convex bodies with the same orientation. We show that the sphere minimizes the dimensionless exclusion volume v ex ( K )/ v ( K ) among all convex bodies, whether randomly oriented or uniformly oriented, for any d , where v ( K ) is the volume of K . When the bodies have the same orientation, the simplex maximizes the dimensionless exclusion volume for any d with a large- d asymptotic scaling behavior of 2 2 d / d 3/2 , which is to be contrasted with the corresponding scaling of 2 d for the sphere. We present explicit formulas for quermassintegrals W 0 ( K ), …, W d ( K ) for many different nonspherical convex bodies, including cubes, parallelepipeds, regular simplices, cross-polytopes, cylinders, spherocylinders, ellipsoids as well as lower-dimensional bodies, such as hyperplates and line segments. These results are utilized to determine the rotationally-averaged exclusion volume v ex ( K ) for these convex-body shapes for dimensions 2 through 12. While the sphere is the shape possessing the minimal dimensionless exclusion volume, we show that, among the convex bodies considered that are sufficiently compact, the simplex possesses the maximal v ex ( K )/ v ( K ) with a scaling behavior of 2 1.6618… d . Subsequently, we apply these results to determine the corresponding second virial coefficient B 2 ( K ) of the aforementioned hard hyperparticles. Our results are also applied to compute estimates of the continuum percolation threshold η c derived previously by the authors for systems of identical overlapping convex bodies. We conjecture that overlapping spheres possess the maximal value of η c among all identical nonzero-volume convex overlapping bodies for d ⩾ 2, randomly or uniformly oriented, and that, among all identical, oriented nonzero-volume convex bodies, overlapping simplices have the minimal value of η c for d ⩾ 2. 

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3. A unifying framework of high-dimensional sparse estimation with dierence-of-convex (DC) regularization

   Cao, Shanshan ; Huo, Xiaoming ; Pang, Jong-Shi ( August 2021 , Statistical science)

   null (Ed.)

   Under the linear regression framework, we study the variable selection problem when the underlying model is assumed to have a small number of nonzero coefficients. Non-convex penalties in specic forms are well-studied in the literature for sparse estimation. A recent work, Ahn, Pang, and Xin (2017), has pointed out that nearly all existing non-convex penalties can be represented as difference-of-convex (DC) functions, which are the difference of two convex functions, while itself may not be convex. There is a large existing literature on optimization problems when their objectives and/or constraints involve DC functions. Efficient numerical solutions have been proposed. Under the DC framework, directional-stationary (d-stationary) solutions are considered, and they are usually not unique. In this paper, we show that under some mild conditions, a certain subset of d-stationary solutions in an optimization problem (with a DC objective) has some ideal statistical properties: namely, asymptotic estimation consistency, asymptotic model selection consistency, asymptotic efficiency. Our assumptions are either weaker than or comparable with those conditions that have been adopted in other existing works. This work shows that DC is a nice framework to offer a unied approach to these existing works where non-convex penalties are involved. Our work bridges the communities of optimization and statistics. 

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4. Constant-hop spanners for more geometric intersection graphs, with even smaller size

   https://doi.org/10.4230/LIPIcs.SoCG.2023.23  

   Chan, Timothy M. ; Huang, Zhengcheng ( June 2023 , Proc. 39th International Symposium on Computational Geometry (SoCG 2023))

   Chambers, Erin W. ; Gudmundsson, Joachim (Ed.)

   In SoCG 2022, Conroy and Tóth presented several constructions of sparse, low-hop spanners in geometric intersection graphs, including an O(nlog n)-size 3-hop spanner for n disks (or fat convex objects) in the plane, and an O(nlog² n)-size 3-hop spanner for n axis-aligned rectangles in the plane. Their work left open two major questions: (i) can the size be made closer to linear by allowing larger constant stretch? and (ii) can near-linear size be achieved for more general classes of intersection graphs? We address both questions simultaneously, by presenting new constructions of constant-hop spanners that have almost linear size and that hold for a much larger class of intersection graphs. More precisely, we prove the existence of an O(1)-hop spanner for arbitrary string graphs with O(nα_k(n)) size for any constant k, where α_k(n) denotes the k-th function in the inverse Ackermann hierarchy. We similarly prove the existence of an O(1)-hop spanner for intersection graphs of d-dimensional fat objects with O(nα_k(n)) size for any constant k and d. We also improve on some of Conroy and Tóth’s specific previous results, in either the number of hops or the size: we describe an O(nlog n)-size 2-hop spanner for disks (or more generally objects with linear union complexity) in the plane, and an O(nlog n)-size 3-hop spanner for axis-aligned rectangles in the plane. Our proofs are all simple, using separator theorems, recursion, shifted quadtrees, and shallow cuttings. 

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5. Algorithmic reconstruction of the fiber of persistent homology on cell complexes

   https://doi.org/10.1007/s41468-024-00165-w  

   Leygonie, Jacob ; Henselman-Petrusek, Gregory ( April 2024 , Journal of Applied and Computational Topology)

   Let Kbe a finite simplicial, cubical, delta or CW complex. The persistence map $$\textrm{PH}$$$\text{PH}$takes a filter $$f:K\rightarrow \mathbb {R}$$$f:K\to R$as input and returns the barcodes of the sublevel set persistent homology of fin each dimension. We address the inverse problem: given target barcodes D, computing the fiber $$\textrm{PH}^{-1}(D)$$${\text{PH}}^{-1}\left(D\right)$. For this, we use the fact that $$\textrm{PH}^{-1}(D)$$${\text{PH}}^{-1}\left(D\right)$decomposes as a polyhedral complex when Kis a simplicial complex, and we generalise this result to arbitrary based chain complexes. We then design and implement a depth-first search that recovers the polytopes forming the fiber $$\textrm{PH}^{-1}(D)$$${\text{PH}}^{-1}\left(D\right)$. As an application, we solve a corpus of 120 sample problems, providing a first insight into the statistical structure of these fibers, for general CW complexes.

    

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