scholarly journals Tight Bounds for Delay-Sensitive Aggregation

2010 ◽  
Vol Vol. 12 no. 1 (Distributed Computing and...) ◽  
Author(s):  
Yvonne Anne Pignolet ◽  
Stefan Schmid ◽  
Roger Wattenhofer
Keyword(s):  
Distributed Computing ◽  
Upper Bound ◽  
Competitive Ratio ◽  
Optimization Problem ◽  
Tree Topology ◽  
Trade Off ◽  
Transmission Cost ◽  
Delay Sensitive ◽  
International Audience ◽  
The Cost

Distributed Computing and Networking International audience This article studies the fundamental trade-off between delay and communication cost in networks. We consider an online optimization problem where nodes are organized in a tree topology. The nodes seek to minimize the time until the root is informed about the changes of their states and to use as few transmissions as possible. We derive an upper bound on the competitive ratio of O(min (h, c)) where h is the tree's height, and c is the transmission cost per edge. Moreover, we prove that this upper bound is tight in the sense that any oblivious algorithm has a ratio of at least Omega(min (h, c)). For chain networks, we prove a tight competitive ratio of Theta(min (root h, c)). Furthermore, we introduce a model for value-sensitive aggregation, where the cost depends on the number of transmissions and the error at the root.

Suboptimality of a Decentralized Feedback Control Law

1999 ◽  
Vol 121 (2) ◽  
pp. 305-308
Author(s):  
El Kebi´r Boukas ◽  
A. Swierniak ◽  
H. Yang
Keyword(s):  
Feedback Control ◽  
Linear System ◽  
Upper Bound ◽  
Optimization Problem ◽  
Control Law ◽  
Relative Cost ◽  
Numerical Example ◽  
Uncertain Linear System ◽  
The Absolute ◽  
The Cost

In this note, a new estimating method for the upper bound of the cost of the uncertain linear system used by Trinh and Aldeen (1993) is proposed. We have also estimated the absolute cost loss and relative cost loss of this kind of optimization problem. To show the usefulness of our results a numerical example has been developed.

scholarly journals Budgeted Optimization with Constrained Experiments

2016 ◽  
Vol 56 ◽  
pp. 119-152 ◽  
Author(s):  
Javad Azimi ◽  
Xiaoli Fern ◽  
Alan Fern
Keyword(s):  
Real World ◽  
Unknown Function ◽  
Optimization Problem ◽  
Experimental Results ◽  
Trade Off ◽  
Input Space ◽  
Real World Problem ◽  
Real Functions ◽  
The Cost

Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(.) given a budget by requesting a sequence of samples from the function. In our setting, however, evaluating the function at precisely specified points is not practically possible due to prohibitive costs. Instead, we can only request constrained experiments. A constrained experiment, denoted by Q, specifies a subset of the input space for the experimenter to sample the function from. The outcome of Q includes a sampled experiment x, and its function output f(x). Importantly, as the constraints of Q become looser, the cost of fulfilling the request decreases, but the uncertainty about the location x increases. Our goal is to manage this trade-off by selecting a set of constrained experiments that best optimize f(.) within the budget. We study this problem in two different settings, the non-sequential (or batch) setting where a set of constrained experiments is selected at once, and the sequential setting where experiments are selected one at a time. We evaluate our proposed methods for both settings using synthetic and real functions. The experimental results demonstrate the efficacy of the proposed methods.

scholarly journals How often should you clean your room?

2015 ◽  
Vol Vol. 17 no. 1 (Analysis of Algorithms) ◽  
Author(s):  
Kimball Martin ◽  
Krishnan Shankar
Keyword(s):  
Combinatorial Optimization ◽  
Upper Bound ◽  
Optimization Problem ◽  
Analysis Of Algorithms ◽  
Combinatorial Optimization Problem ◽  
Optimal Time ◽  
International Audience ◽  
Approximate Version

Analysis of Algorithms International audience We introduce and study a combinatorial optimization problem motivated by the question in the title. In the simple case where you use all objects in your room equally often, we investigate asymptotics of the optimal time to clean up in terms of the number of objects in your room. In particular, we prove a logarithmic upper bound, solve an approximate version of this problem, and conjecture a precise logarithmic asymptotic.

Developing Schedule With Linear Programming (Case Study: STTF II Project Komplek Sukamukti Banjaran)

2020 ◽  
Vol 4 (02) ◽  
pp. 34-45
Author(s):  
Naufal Dzikri Afifi ◽  
Ika Arum Puspita ◽  
Mohammad Deni Akbar
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Project Scheduling ◽  
Time Limit ◽  
Minimum Time ◽  
Service Cost ◽  
Trade Off ◽  
Overtime Work ◽  
The Cost

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

scholarly journals Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls

2020 ◽  
Vol 12 (7) ◽  
pp. 2767 ◽  
Author(s):  
Víctor Yepes ◽  
José V. Martí ◽  
José García
Keyword(s):  
Black Hole ◽  
Sustainable Design ◽  
Retaining Walls ◽  
The Other ◽  
Trade Off ◽  
Construction Company ◽  
Geometric Variables ◽  
The Stability ◽  
The Cost ◽  
Black Hole Algorithm

The optimization of the cost and CO 2 emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min–max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO 2 emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.

Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost‐service trade‐off

2021 ◽  
Author(s):  
Vincent E. Castillo ◽  
John E. Bell ◽  
Diane A. Mollenkopf ◽  
Theodore P. Stank
Keyword(s):  
Service Trade ◽  
Trade Off ◽  
Last Mile ◽  
The Cost ◽  
Last Mile Delivery ◽  
Systems View

Fast Water Transport Through Sub-5 nm Polyamide Nanofilms: The New Upper-Bound of the Permeance-Selectivity Trade-Off in Nanofiltration

2021 ◽  
Author(s):  
Pulak Sarkar ◽  
Solagna Modak ◽  
Santanu Ray ◽  
Vasista Adupa ◽  
K. Anki Reddy ◽  
...  
Keyword(s):  
Water Transport ◽  
Composite Membrane ◽  
Upper Bound ◽  
Polymer Membranes ◽  
Liquid Transport ◽  
Trade Off ◽  
Commercial Exploitation

Liquid transport through the composite membrane is inversely proportional to the thickness of its separation layer. While the scalable fabrication of ultrathin polymer membranes is sought for their commercial exploitation,...

scholarly journals Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint

2021 ◽  
Vol 5 (1) ◽  
pp. 1-22
Author(s):  
Jing Tang ◽  
Xueyan Tang ◽  
Andrew Lim ◽  
Kai Han ◽  
Chongshou Li ◽  
...  
Keyword(s):  
Approximation Algorithms ◽  
Greedy Algorithm ◽  
Branch And Bound ◽  
Upper Bound ◽  
Optimization Problem ◽  
Approximation Factor ◽  
Real World Application ◽  
Efficiency Of Algorithms ◽  
Knapsack Constraint ◽  
Submodular Maximization

Monotone submodular maximization with a knapsack constraint is NP-hard. Various approximation algorithms have been devised to address this optimization problem. In this paper, we revisit the widely known modified greedy algorithm. First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factors of 0.357 given by Wolsey and (1-1/e)/2\approx 0.316 given by Khuller et al. More importantly, our analysis closes a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/\sqrte )\approx 0.393 in the literature to clarify a long-standing misconception on this issue. Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum. We empirically demonstrate the tightness of our upper bound with a real-world application. The bound enables us to obtain a data-dependent ratio typically much higher than 0.405 between the solution value of the modified greedy algorithm and the optimum. It can also be used to significantly improve the efficiency of algorithms such as branch and bound.

scholarly journals Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeonghyuk Park ◽  
Yul Ri Chung ◽  
Seo Taek Kong ◽  
Yeong Won Kim ◽  
Hyunho Park ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Morphological Analysis ◽  
Deep Neural Networks ◽  
Large Datasets ◽  
Histopathological Diagnosis ◽  
Single Model ◽  
Trade Off ◽  
Detection Model ◽  
Optimal Behavior ◽  
The Cost

AbstractThere have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

scholarly journals A trade-off between classical and quantum circuit size for an attack against CSIDH

2020 ◽  
Vol 15 (1) ◽  
pp. 4-17
Author(s):  
Jean-François Biasse ◽  
Xavier Bonnetain ◽  
Benjamin Pring ◽  
André Schrottenloher ◽  
William Youmans
Keyword(s):  
Endomorphism Ring ◽  
State Of The Art ◽  
Quantum Circuit ◽  
List Type ◽  
Hard Problem ◽  
Trade Off ◽  
Classical State ◽  
Security Parameter ◽  
The Cost ◽  
Quadratic Order

AbstractWe propose a heuristic algorithm to solve the underlying hard problem of the CSIDH cryptosystem (and other isogeny-based cryptosystems using elliptic curves with endomorphism ring isomorphic to an imaginary quadratic order 𝒪). Let Δ = Disc(𝒪) (in CSIDH, Δ = −4p for p the security parameter). Let 0 < α < 1/2, our algorithm requires:A classical circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{1-\alpha}\right)}.$A quantum circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{\alpha}\right)}.$Polynomial classical and quantum memory.Essentially, we propose to reduce the size of the quantum circuit below the state-of-the-art complexity $2^{\tilde{O}\left(\log(|\Delta|)^{1/2}\right)}$ at the cost of increasing the classical circuit-size required. The required classical circuit remains subexponential, which is a superpolynomial improvement over the classical state-of-the-art exponential solutions to these problems. Our method requires polynomial memory, both classical and quantum.

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