ترغب بنشر مسار تعليمي؟ اضغط هنا

Convex hull algorithms based on some variational models

85   0   0.0 ( 0 )
 نشر من قبل Lingfeng Li
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Seeking the convex hull of an object is a very fundamental problem arising from various tasks. In this work, we propose two variational convex hull models using level set representation for 2-dimensional data. The first one is an exact model, which can get the convex hull of one or multiple objects. In this model, the convex hull is characterized by the zero sublevel-set of a convex level set function, which is non-positive at every given point. By minimizing the area of the zero sublevel-set, we can find the desired convex hull. The second one is intended to get convex hull of objects with outliers. Instead of requiring all the given points are included, this model penalizes the distance from each given point to the zero sublevel-set. Literature methods are not able to handle outliers. For the solution of these models, we develop efficient numerical schemes using alternating direction method of multipliers. Numerical examples are given to demonstrate the advantages of the proposed methods.



قيم البحث

اقرأ أيضاً

82 - Haitao Wang 2020
In this paper, we first consider the subpath convex hull query problem: Given a simple path $pi$ of $n$ vertices, preprocess it so that the convex hull of any query subpath of $pi$ can be quickly obtained. Previously, Guibas, Hershberger, and Snoeyin k [SODA 90] proposed a data structure of $O(n)$ space and $O(log nloglog n)$ query time; reducing the query time to $O(log n)$ increases the space to $O(nloglog n)$. We present an improved result that uses $O(n)$ space while achieving $O(log n)$ query time. Like the previous work, our query algorithm returns a compact interval tree representing the convex hull so that standard binary-search-based queries on the hull can be performed in $O(log n)$ time each. Our new result leads to improvements for several other problems. In particular, with the help of the above result, we present new algorithms for the ray-shooting problem among segments. Given a set of $n$ (possibly intersecting) line segments in the plane, preprocess it so that the first segment hit by a query ray can be quickly found. We give a data structure of $O(nlog n)$ space that can answer each query in $(sqrt{n}log n)$ time. If the segments are nonintersecting or if the segments are lines, then the space can be reduced to $O(n)$. All these are classical problems that have been studied extensively. Previously data structures of $widetilde{O}(sqrt{n})$ query time (the notation $widetilde{O}$ suppresses a polylogarithmic factor) were known in early 1990s; nearly no progress has been made for over two decades. For all problems, our results provide improvements by reducing the space of the data structures by at least a logarithmic factor while the preprocessing and query times are the same as before or even better.
273 - Jer^ome Leroux 2008
Arithmetic automata recognize infinite words of digits denoting decompositions of real and integer vectors. These automata are known expressive and efficient enough to represent the whole set of solutions of complex linear constraints combining both integral and real variables. In this paper, the closed convex hull of arithmetic automata is proved rational polyhedral. Moreover an algorithm computing the linear constraints defining these convex set is provided. Such an algorithm is useful for effectively extracting geometrical properties of the whole set of solutions of complex constraints symbolically represented by arithmetic automata.
Given a finite set of points $P subseteq mathbb{R}^d$, we would like to find a small subset $S subseteq P$ such that the convex hull of $S$ approximately contains $P$. More formally, every point in $P$ is within distance $epsilon$ from the convex hul l of $S$. Such a subset $S$ is called an $epsilon$-hull. Computing an $epsilon$-hull is an important problem in computational geometry, machine learning, and approximation algorithms. In many real world applications, the set $P$ is too large to fit in memory. We consider the streaming model where the algorithm receives the points of $P$ sequentially and strives to use a minimal amount of memory. Existing streaming algorithms for computing an $epsilon$-hull require $O(epsilon^{-(d-1)/2})$ space, which is optimal for a worst-case input. However, this ignores the structure of the data. The minimal size of an $epsilon$-hull of $P$, which we denote by $text{OPT}$, can be much smaller. A natural question is whether a streaming algorithm can compute an $epsilon$-hull using only $O(text{OPT})$ space. We begin with lower bounds that show that it is not possible to have a single-pass streaming algorithm that computes an $epsilon$-hull with $O(text{OPT})$ space. We instead propose three relaxations of the problem for which we can compute $epsilon$-hulls using space near-linear to the optimal size. Our first algorithm for points in $mathbb{R}^2$ that arrive in random-order uses $O(log ncdot text{OPT})$ space. Our second algorithm for points in $mathbb{R}^2$ makes $O(log(frac{1}{epsilon}))$ passes before outputting the $epsilon$-hull and requires $O(text{OPT})$ space. Our third algorithm for points in $mathbb{R}^d$ for any fixed dimension $d$ outputs an $epsilon$-hull for all but $delta$-fraction of directions and requires $O(text{OPT} cdot log text{OPT})$ space.
Distributed operation of integrated electricity and gas systems (IEGS) receives much attention since it respects data security and privacy between different agencies. This paper proposes an extended convex hull (ECH) based method to address the distr ibuted optimal energy flow (OEF) problem in the IEGS. First, a multi-block IEGS model is obtained by dividing it into N blocks according to physical and regional differences. This multi-block model is then convexified by replacing the nonconvex gas transmission equation with its ECH-based constraints. The Jacobi-Proximal alternating direction method of multipliers (J-ADMM) algorithm is adopted to solve the convexified model and minimize its operation cost. Finally, the feasibility of the optimal solution for the convexified problem is checked, and a sufficient condition is developed. The optimal solution for the original nonconvex problem is recovered from that for the convexified problem if the sufficient condition is satisfied. Test results reveal that this method is tractable and effective in obtaining the feasible optimal solution for radial gas networks.
High-throughput computational materials searches generate large databases of locally-stable structures. Conventionally, the needle-in-a-haystack search for the few experimentally-synthesizable compounds is performed using a convex hull construction, which identifies structures stabilized by manipulation of a particular thermodynamic constraint (for example pressure or composition) chosen based on prior experimental evidence or intuition. To address the biased nature of this procedure we introduce a generalized convex hull framework. Convex hulls are constructed on data-driven principal coordinates, which represent the full structural diversity of the database. Their coupling to experimentally-realizable constraints hints at the conditions that are most likely to stabilize a given configuration. The probabilistic nature of our framework also addresses the uncertainty stemming from the use of approximate models during database construction, and eliminates redundant structures. The remaining small set of candidates that have a high probability of being synthesizable provide a much needed starting point for the determination of viable synthetic pathways.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا