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1. Faster exact and approximation algorithms for packing and covering matroids via push-relabel

   https://doi.org/10.1137/1.9781611977912.82  

   Quanrud, Kent (January 2024, Proceedings of the 2024 ACM-SIAM Symposium on Discrete Algorithms, SODA 2024, Alexandria, VA, USA, January 7-10, 2024)

   Woodruff, David P. (Ed.)

   Matroids are a fundamental object of study in combinatorial optimization. Three closely related and important problems involving matroids are maximizing the size of the union of $$k$$ independent sets (that is, \emph{$$k$$-fold matroid union}), computing $$k$$ disjoint bases (a.k.a.\ \emph{matroid base packing}), and covering the elements by $$k$$ bases (a.k.a.\ \emph{matroid base covering}). These problems generalize naturally to integral and real-valued capacities on the elements. This work develops faster exact and/or approximation problems for these and some other closely related problems such as optimal reinforcement and matroid membership. We obtain improved running times both for general matroids in the independence oracle model and for the graphic matroid. The main thrust of our improvements comes from developing a faster and unifying \emph{push-relabel} algorithm for the integer-capacitated versions of these problems, building on previous work by [FM12]. We then build on this algorithm in two directions. First we develop a faster augmenting path subroutine for $$k$$-fold matroid union that, when appended to an approximation version of the push-relabel algorithm, gives a faster exact algorithm for some parameters of $$k$$. In particular we obtain a subquadratic-query running time in the uncapacitated setting for the three basic problems listed above. We also obtain faster approximation algorithms for these problems with real-valued capacities by reducing to small integral capacities via randomized rounding. To this end, we develop a new randomized rounding technique for base covering problems in matroids that may also be of independent interest. 

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2. Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems

   https://doi.org/10.1137/1.9781611975994.52  

   Abbasi-Zadeh, S; Bansal, N; Guruganesh, G.; Nikolov, A; Schwartz, R; Singh, M. (January 2020, Proceedings of the Annual ACMSIAM Symposium on Discrete Algorithms)

   Semi-definite programming is a powerful tool in the design and analysis of approximation algorithms for combinatorial optimization problems. In particular, the random hyperplane rounding method of Goemans and Williamson [23] has been extensively studied for more than two decades, resulting in various extensions to the original technique and beautiful algorithms for a wide range of applications. Despite the fact that this approach yields tight approximation guarantees for some problems, e.g., Max-Cut, for many others, e.g., Max-SAT and Max-DiCut, the tight approximation ratio is still unknown. One of the main reasons for this is the fact that very few techniques for rounding semi-definite relaxations are known. In this work, we present a new general and simple method for rounding semi-definite programs, based on Brownian motion. Our approach is inspired by recent results in algorithmic discrepancy theory. We develop and present tools for analyzing our new rounding algorithms, utilizing mathematical machinery from the theory of Brownian motion, complex analysis, and partial differential equations. Focusing on constraint satisfaction problems, we apply our method to several classical problems, including Max-Cut, Max-2SAT, and Max-DiCut, and derive new algorithms that are competitive with the best known results. To illustrate the versatility and general applicability of our approach, we give new approximation algorithms for the Max-Cut problem with side constraints that crucially utilizes measure concentration results for the Sticky Brownian Motion, a feature missing from hyperplane rounding and its generalizations. 

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3. Enclosing Points with Geometric Objects

   https://doi.org/10.4230/LIPIcs.SoCG.2024.35  

   Chan, Timothy M; He, Qizheng; Xue, Jie (June 2024, Proc. 40th Sympos. Computational Geometry (SoCG))

   Mulzer, Wolfgang; Phillips, Jeff M (Ed.)

   Let X be a set of points in ℝ² and 𝒪 be a set of geometric objects in ℝ², where |X| + |𝒪| = n. We study the problem of computing a minimum subset 𝒪^* ⊆ 𝒪 that encloses all points in X. Here a point x ∈ X is enclosed by 𝒪^* if it lies in a bounded connected component of ℝ²∖(⋃_{O ∈ 𝒪^*} O). We propose two algorithmic frameworks to design polynomial-time approximation algorithms for the problem. The first framework is based on sparsification and min-cut, which results in O(1)-approximation algorithms for unit disks, unit squares, etc. The second framework is based on LP rounding, which results in an O(α(n)log n)-approximation algorithm for segments, where α(n) is the inverse Ackermann function, and an O(log n)-approximation algorithm for disks. 

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4. Linear Programming based Reductions for Multiple Visit TSP and Vehicle Routing Problems \title{Linear Programming based Reductions for Multiple Visit TSP and Vehicle Routing Problems

   Pillia, A; Singh, M (April 2025, Arxiv)

   The multiple traveling salesman problem (mTSP) is an important variant of metric TSP where a set of k salespeople together visit a set of n cities while minimizing the total cost of the k routes under a given cost metric. The mTSP problem has applications to many real-life problems such as vehicle routing. Rothkopf [14] introduced another variant of TSP called many-visits TSP (MV-TSP) where a request r(v) is given for each city v and a single salesperson needs to visit each city r(v) times and return to his starting point. We note that in MV-TSP the cost of loops is positive, so a TSP solution cannot be trivially extended (without an increase in cost) to a MV-TSP solution by consecutively visiting each vertex to satisfy the visit requirements. A combination of mTSP and MV-TSP, called many-visits multiple TSP (MV-mTSP) was studied by Berczi, Mnich, and Vincze [3] where the authors give approximation algorithms for various variants of MV-mTSP. In this work, we show a simple linear programming (LP) based reduction that converts a mTSP LP-based algorithm to an LP-based algorithm for MV-mTSP with the same approximation factor. We apply this reduction to improve or match the current best approximation factors of several variants of the MV-mTSP. Our reduction shows that the addition of visit requests r(v) to mTSP does not make the problem harder to approximate even when r(v) is exponential in the number of vertices. To apply our reduction, we either use existing LP-based algorithms for mTSP variants or show that several existing combinatorial algorithms for mTSP variants can be interpreted as LP-based algorithms. This allows us to apply our reduction to these combinatorial algorithms while achieving improved guarantees. 

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5. Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization

   https://doi.org/10.14778/3641204.3641222  

   Mai, Anh L; Wang, Pengyu; Abouzied, Azza; Brucato, Matteo; Haas, Peter J; Meliou, Alexandra (January 2024, Proceedings of the VLDB Endowment)

   A package query returns a package---a multiset of tuples---that maximizes or minimizes a linear objective function subject to linear constraints, thereby enabling in-database decision support. Prior work has established the equivalence of package queries to Integer Linear Programs (ILPs) and developed the SketchRefine algorithm for package query processing. While this algorithm was an important first step toward supporting prescriptive analytics scalably inside a relational database, it struggles when the data size grows beyond a few hundred million tuples or when the constraints become very tight. In this paper, we present Progressive Shading, a novel algorithm for processing package queries that can scale efficiently to billions of tuples and gracefully handle tight constraints. Progressive Shading solves a sequence of optimization problems over a hierarchy of relations, each resulting from an ever-finer partitioning of the original tuples into homogeneous groups until the original relation is obtained. This strategy avoids the premature discarding of high-quality tuples that can occur with SketchRefine. Our novel partitioning scheme, Dynamic Low Variance, can handle very large relations with multiple attributes and can dynamically adapt to both concentrated and spread-out sets of attribute values, provably outperforming traditional partitioning schemes such as kd-tree. We further optimize our system by replacing our off-the-shelf optimization software with customized ILP and LP solvers, called Dual Reducer and Parallel Dual Simplex respectively, that are highly accurate and orders of magnitude faster. 

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