arXiv subjects

Tiow-Seng Tan

Publications and source records attributed to Tiow-Seng Tan.

5 recordsLinked to original sources

arXiv2025

Smallest Intersecting and Enclosing Balls

We study the smallest intersecting and enclosing ball problems in Euclidean spaces for input objects that are compact and convex. They link and unify many problems in computational geometry and machine learning. We show that both problems can be modeled as zero-sum games, and propose an approximation algorithm for the former. Specifically, the algorithm produces the first results in high-dimensional spaces for various input objects such as convex polytopes, balls, ellipsoids, etc.

cs.CG↗

arXiv2024

Approximation Algorithms for Smallest Intersecting Balls

We study a general smallest intersecting ball problem and its soft-margin variant in high-dimensional Euclidean spaces for input objects that are compact and convex. These two problems link and unify a series of fundamental problems in computational geometry and machine learning, including smallest enclosing ball, polytope distance, intersection radius, $\ell_1$-loss support vector machine, $\ell_1$-loss support vector data description, and so on. Leveraging our novel framework for solving zero-sum games over symmetric cones, we propose general approximation algorithms for the two problems, where implementation details are presented for specific inputs of convex polytopes, reduced polytopes, axis-aligned bounding boxes, balls, and ellipsoids. For most of these inputs, our algorithms are the first results in high-dimensional spaces, and also the first approximation methods. Experimental results show that our algorithms can solve large-scale input instances efficiently.

cs.CG↗

arXiv2024

A Primal-Dual Framework for Symmetric Cone Programming

In this paper, we introduce a primal-dual algorithmic framework for solving Symmetric Cone Programs (SCPs), a versatile optimization model that unifies and extends Linear, Second-Order Cone (SOCP), and Semidefinite Programming (SDP). Our work generalizes the primal-dual framework for SDPs introduced by Arora and Kale, leveraging a recent extension of the Multiplicative Weights Update method (MWU) to symmetric cones. Going beyond existing works, our framework can handle SOCPs and mixed SCPs, exhibits nearly linear time complexity, and can be effectively parallelized. To illustrate the efficacy of our framework, we employ it to develop approximation algorithms for two geometric optimization problems: the Smallest Enclosing Sphere problem and the Support Vector Machine problem. Our theoretical analyses demonstrate that the two algorithms compute approximate solutions in nearly linear running time and with parallel depth scaling polylogarithmically with the input size. We compare our algorithms against CGAL as well as interior point solvers applied to these problems. Experiments show that our algorithms are highly efficient when implemented on a CPU and achieve substantial speedups when parallelized on a GPU, allowing us to solve large-scale instances of these problems.

math.OC↗

arXiv2020

On Designing GPU Algorithms with Applications to Mesh Refinement

We present a set of rules to guide the design of GPU algorithms. These rules are grounded on the principle of reducing waste in GPU utility to achieve good speed up. In accordance to these rules, we propose GPU algorithms for 2D constrained, 3D constrained and 3D Restricted Delaunay refinement problems respectively. Our algorithms take a 2D planar straight line graph (PSLG) or 3D piecewise linear complex (PLC) $\mathcal{G}$ as input, and generate quality meshes conforming or approximating to $\mathcal{G}$. The implementation of our algorithms shows that they are the first to run an order of magnitude faster than current state-of-the-art counterparts in sequential and parallel manners while using similar numbers of Steiner points to produce triangulations of comparable qualities. It thus reduces the computing time of mesh refinement from possibly hours to a few seconds or minutes for possible use in interactive graphics applications.

cs.GR↗

arXiv2019

Computing Three-dimensional Constrained Delaunay Refinement Using the GPU

We propose the first GPU algorithm for the 3D triangulation refinement problem. For an input of a piecewise linear complex $\mathcal{G}$ and a constant $B$, it produces, by adding Steiner points, a constrained Delaunay triangulation conforming to $\mathcal{G}$ and containing tetrahedra mostly of radius-edge ratios smaller than $B$. Our implementation of the algorithm shows that it can be an order of magnitude faster than the best CPU algorithm while using a similar amount of Steiner points to produce triangulations of comparable quality.

cs.GR↗