An official website of the United States government
Abstract The classic double bubble theorem says that the least-perimeter way to enclose and separate two prescribed volumes in ℝ N is the standard double bubble. We seek the optimal double bubble in ℝ N with density, which we assume to be strictly log-convex. For N = 1 we show that the solution is sometimes two contiguous intervals and sometimes three contiguous intervals. In higher dimensions we think that the solution is sometimes a standard double bubble and sometimes concentric spheres (e.g. for one volume small and the other large).
more »
« less
Double bubbles on the line with log-convex density f with (logf)′ bounded
We extend results of Bongiovanni et al. [1] on double bubbles on the line with log-convex density to the case where the derivative of the log of the density is bounded. We show that the tie function between the double interval and the triple interval still exists, but may blow up to infinity in finite time. For the first time, a density is presented for which the blowup time is positive and finite.
more »
« less
- Award ID(s):
- 1659037
- PAR ID:
- 10092875
- Date Published:
- Journal Name:
- Missouri journal of mathematical sciences
- Volume:
- 30
- ISSN:
- 1085-2581
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Interval scheduling is a basic problem in the theory of algorithms and a classical task in combinatorial optimization. We develop a set of techniques for partitioning and grouping jobs based on their starting and ending times, that enable us to view an instance of interval scheduling on many jobs as a union of multiple interval scheduling instances, each containing only a few jobs. Instantiating these techniques in dynamic and local settings of computation leads to several new results. For (1+ε)-approximation of job scheduling of n jobs on a single machine, we develop a fully dynamic algorithm with O((log n)/ε) update and O(log n) query worst-case time. Further, we design a local computation algorithm that uses only O((log N)/ε) queries when all jobs are length at least 1 and have starting/ending times within [0,N]. Our techniques are also applicable in a setting where jobs have rewards/weights. For this case we design a fully dynamic deterministic algorithm whose worst-case update and query time are poly(log n,1/ε). Equivalently, this is the first algorithm that maintains a (1+ε)-approximation of the maximum independent set of a collection of weighted intervals in poly(log n,1/ε) time updates/queries. This is an exponential improvement in 1/ε over the running time of a randomized algorithm of Henzinger, Neumann, and Wiese [SoCG, 2020], while also removing all dependence on the values of the jobs' starting/ending times and rewards, as well as removing the need for any randomness. We also extend our approaches for interval scheduling on a single machine to examine the setting with M machines.more » « less
-
Interval scheduling is a basic problem in the theory of algorithms and a classical task in combinatorial optimization. We develop a set of techniques for partitioning and grouping jobs based on their starting and ending times, that enable us to view an instance of interval scheduling on many jobs as a union of multiple interval scheduling instances, each containing only a few jobs. Instantiating these techniques in dynamic and local settings of computation leads to several new results. For (1+ε)-approximation of job scheduling of n jobs on a single machine, we develop a fully dynamic algorithm with O((log n)/ε) update and O(log n) query worst-case time. Further, we design a local computation algorithm that uses only O((log N)/ε) queries when all jobs are length at least 1 and have starting/ending times within [0,N]. Our techniques are also applicable in a setting where jobs have rewards/weights. For this case we design a fully dynamic deterministic algorithm whose worst-case update and query time are poly(log n,1/ε). Equivalently, this is the first algorithm that maintains a (1+ε)-approximation of the maximum independent set of a collection of weighted intervals in poly(log n,1/ε) time updates/queries. This is an exponential improvement in 1/ε over the running time of a randomized algorithm of Henzinger, Neumann, and Wiese [SoCG, 2020], while also removing all dependence on the values of the jobs' starting/ending times and rewards, as well as removing the need for any randomness. We also extend our approaches for interval scheduling on a single machine to examine the setting with M machines.more » « less
-
Woodruff, David P. (Ed.)We give improved algorithms for maintaining edge-orientations of a fully-dynamic graph, such that the maximum out-degree is bounded. On one hand, we show how to orient the edges such that maximum out-degree is proportional to the arboricity $$\alpha$$ of the graph, in, either, an amortised update time of $$O(\log^2 n \log \alpha)$$, or a worst-case update time of $$O(\log^3 n \log \alpha)$$. On the other hand, motivated by applications including dynamic maximal matching, we obtain a different trade-off. Namely, the improved update time of either $$O(\log n \log \alpha)$$, amortised, or $$O(\log ^2 n \log \alpha)$$, worst-case, for the problem of maintaining an edge-orientation with at most $$O(\alpha + \log n)$$ out-edges per vertex. Finally, all of our algorithms naturally limit the recourse to be polylogarithmic in $$n$$ and $$\alpha$$. Our algorithms adapt to the current arboricity of the graph, and yield improvements over previous work: Firstly, we obtain deterministic algorithms for maintaining a $$(1+\varepsilon)$$ approximation of the maximum subgraph density, $$\rho$$, of the dynamic graph. Our algorithms have update times of $$O(\varepsilon^{-6}\log^3 n \log \rho)$$ worst-case, and $$O(\varepsilon^{-4}\log^2 n \log \rho)$$ amortised, respectively. We may output a subgraph $$H$$ of the input graph where its density is a $$(1+\varepsilon)$$ approximation of the maximum subgraph density in time linear in the size of the subgraph. These algorithms have improved update time compared to the $$O(\varepsilon^{-6}\log ^4 n)$$ algorithm by Sawlani and Wang from STOC 2020. Secondly, we obtain an $$O(\varepsilon^{-6}\log^3 n \log \alpha)$$ worst-case update time algorithm for maintaining a $$(1~+~\varepsilon)\textnormal{OPT} + 2$$ approximation of the optimal out-orientation of a graph with adaptive arboricity $$\alpha$$, improving the $$O(\varepsilon^{-6}\alpha^2 \log^3 n)$$ algorithm by Christiansen and Rotenberg from ICALP 2022. This yields the first worst-case polylogarithmic dynamic algorithm for decomposing into $$O(\alpha)$$ forests. Thirdly, we obtain arboricity-adaptive fully-dynamic deterministic algorithms for a variety of problems including maximal matching, $$\Delta+1$$ colouring, and matrix vector multiplication. All update times are worst-case $$O(\alpha+\log^2n \log \alpha)$$, where $$\alpha$$ is the current arboricity of the graph. For the maximal matching problem, the state-of-the-art deterministic algorithms by Kopelowitz, Krauthgamer, Porat, and Solomon from ICALP 2014 runs in time $$O(\alpha^2 + \log^2 n)$$, and by Neiman and Solomon from STOC 2013 runs in time $$O(\sqrt{m})$$. We give improved running times whenever the arboricity $$\alpha \in \omega( \log n\sqrt{\log\log n})$$.more » « less
-
Abstract For many planar bipedal models, each step is divided into a finite time single support period and an instantaneous double support period. During single support, the biped is typically underactuated and thus has limited ability to reject disturbances. The instantaneous nature of the double support period prevents nonimpulsive control during this period. However, if the double support period is expanded to finite time, it becomes overactuated. While it has been hypothesized that this overactuation during a finite-time double support period may improve disturbance rejection capabilities, this has not yet been tested. This paper presents a refined biped model by developing a finite-time, adaptive double support controller capable of handling the overactuation and limiting slip. Using simulations, we quantify the disturbance rejection capabilities of this controller and directly compare them to a typical, instantaneous double support model for a range of gait speeds and perturbations. We find that the finite-time double support controller increased the walking stability of the biped in approximately half of the cases, indicating that a finite-time double support period does not automatically increase disturbance rejection capabilities. We also find that the timing and magnitude of the perturbation can affect if a finite-time double support period enhances stability. Finally, we demonstrate that the adaptive controller reduces slipping.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
