scholarly journals Navigating in Trees with Permanently Noisy Advice

2021 ◽  
Vol 17 (2) ◽  
pp. 1-27
Author(s):  
Lucas Boczkowski ◽  
Uriel Feige ◽  
Amos Korman ◽  
Yoav Rodeh
Keyword(s):  
Phase Transition ◽  
Maximum Degree ◽  
Search Strategy ◽  
Partial Information ◽  
Search Algorithm ◽  
Search Problem ◽  
Expected Number ◽  
Noise Parameter ◽  
Faulty Node

We consider a search problem on trees in which an agent starts at the root of a tree and aims to locate an adversarially placed treasure, by moving along the edges, while relying on local, partial information. Specifically, each node in the tree holds a pointer to one of its neighbors, termed advice . A node is faulty with probability q . The advice at a non-faulty node points to the neighbor that is closer to the treasure, and the advice at a faulty node points to a uniformly random neighbor. Crucially, the advice is permanent , in the sense that querying the same node again would yield the same answer. Let Δ denote the maximum degree. For the expected number of moves (edge traversals) until finding the treasure, we show that a phase transition occurs when the noise parameter q is roughly 1 √Δ. Below the threshold, there exists an algorithm with expected number of moves O ( D √Δ), where D is the depth of the treasure, whereas above the threshold, every search algorithm has an expected number of moves, which is both exponential in D and polynomial in the number of nodes  n . In contrast, if we require to find the treasure with probability at least 1 − δ, then for every fixed ɛ > 0, if q < 1/Δ ɛ , then there exists a search strategy that with probability 1 − δ finds the treasure using (Δ −1 D ) O (1/ε) moves. Moreover, we show that (Δ −1 D ) Ω(1/ε) moves are necessary.

Strategy Study on Maximum Degree and Bisection Degree Synchronization Search in Scale-Free Networks

2013 ◽  
Vol 303-306 ◽  
pp. 2157-2160
Author(s):  
Xin Yi Chen ◽  
Jian Hua Xia
Keyword(s):  
Maximum Degree ◽  
Search Strategy ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Search Efficiency ◽  
Network Congestion ◽  
Breadth First Search ◽  
Strategy Study ◽  
Scale Free ◽  
Search Speed

With the expansion of network and the increasing number of communities’ network, It’s a big problem for the search algorithm to enhance the search efficiency. The number of search steps and the amount of query information generated by maximum degree search strategy, which will grow exponentially, consequently, and lead to low the efficiency of search. Without considering the network congestion, breadth-first search strategy is undoubtedly the best search efficiency. From the point of the breadth-first search strategy, this paper designed and proposed the synchronous search strategy of Maximum degree and Bisection degree, and described the algorithm idea and algorithm design for MBDS. The simulation results showed that MBDS not only decreased the amount of query information, but also can efficiently decrease the search steps and improve the search speed.

EXPRESS: Baseline Correction Based on a Search Algorithm from Artificial Intelligence

2020 ◽  
pp. 000370282097751
Author(s):  
Xin Wang ◽  
Xia Chen
Keyword(s):  
Artificial Intelligence ◽  
Objective Function ◽  
Least Squares ◽  
Least Squares Method ◽  
Search Algorithm ◽  
Absolute Error ◽  
Search Problem ◽  
Baseline Correction ◽  
Current Spectrum ◽  
Data Points

Many spectra have a polynomial-like baseline. Iterative polynomial fitting (IPF) is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by IPF may have substantially error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, IPF uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence (AI) to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error (MAE) as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods: Lieber and Mahadevan–Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real FTIR and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra, the baseline estimated by SA has fewer error than those by the three other methods.

scholarly journals Implementation of Grover’s Quantum Search Algorithm using Rigetti Forest

2019 ◽  
Vol 8 (6S3) ◽  
pp. 110-114
Keyword(s):  
Quantum Computing ◽  
Search Algorithm ◽  
Search Problem ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Search Result ◽  
Amplitude Amplification ◽  
Testing And Evaluation

Grover’s quantum search algorithm allows quadratic speedup in unsorted search problem by utilizing amplitude amplification trick in quantum computing. In this paper, an approach to implement Grover’s quantum search algorithm is proposed. The implementation is done using Rigetti Forest and Python. The testing and evaluation processes are carried on in two computers with different hardware specifications to derive more information from the result. The results are measured in user time and compared with implementation from Quantum Computing Playground. The user time of this implementation for 10 qubits and 1024 data is slower compared to Quantum Computing Playground’s implementation. The proposed implementation can be improved by calculating the probability of Grover’s quantum search algorithm in finding the appropriate search result.

scholarly journals Active Disturbance Rejection Control Design Using the Optimization Algorithm for a Hydraulic Quadruped Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Yuqi Fan ◽  
Junpeng Shao ◽  
Guitao Sun ◽  
Xuan Shao
Keyword(s):  
Disturbance Rejection ◽  
Search Algorithm ◽  
Parameter Tuning ◽  
Quadruped Robot ◽  
Search Problem ◽  
Lévy Flight ◽  
Control Performance ◽  
External Interference ◽  
Levy Flight ◽  
Active Disturbance Rejection

To increase the robustness and control precision of a hydraulic quadruped robot and simultaneously enhance the dynamic and steady characteristic of the hydraulic system, an active disturbance rejection controller (ADRC) tuned using the Lévy-flight beetle antennae search algorithm (LBAS) was proposed. Moreover, the designed controller was used in the hydraulic quadruped robot to enhance the control performance and restrain the disturbances. The use of the Lévy-flight trajectory in the advanced algorithm can help increase the search speed and iteration accuracy. In the LBAS-ADRC, the parameter tuning method is adopted to develop the active disturbance rejection controller enhanced using the beetle antennae search algorithm. When implemented in the hydraulic quadruped robot, the LBAS-ADRC can ensure satisfactory dynamic characteristics and stability in the presence of external interference. In particular, in the proposed method, the ADRC parameter search problem is transformed to a sixteen-dimensional search problem, the solution of which is identified using the Lévy-flight beetle antennae search algorithm. Moreover, three different algorithms are implemented in the active disturbance rejection controller tuning problem to demonstrate the control performance of the proposed controller. The analysis results show that the proposed controller can achieve a small amplitude overshoot under complex and changeable environments.

scholarly journals Learning Descriptors Invariance through Equivalence Relations within Manifold: A New Approach to Expression Invariant 3D Face Recognition

2020 ◽  
Vol 6 (11) ◽  
pp. 112
Author(s):  
Faisal R. Al-Osaimi
Keyword(s):  
Search Algorithm ◽  
Recognition Performance ◽  
Feature Space ◽  
Training Data ◽  
Equivalence Relations ◽  
Graph Search ◽  
Search Problem ◽  
New Approach ◽  
Unique Approach ◽  
Training Examples

This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins.

IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments

2020 ◽  
Vol 88 ◽  
pp. 105945 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Akshya Kaveri ◽  
Akshay Rahul K. ◽  
Kannan Krithivasan ◽  
...  
Keyword(s):  
Search Strategy ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cloud Environments ◽  
Gravitational Search

scholarly journals Directional-Sensor Network Deployment Planning for Mobile-Target Search

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 82
Author(s):  
Shiraz Wasim ◽  
Zendai Kashino ◽  
Goldie Nejat ◽  
Beno Benhabib
Keyword(s):  
Sensor Network ◽  
Search Algorithm ◽  
Search Problem ◽  
Target Search ◽  
Physical Constraints ◽  
Sensing Model ◽  
Mobile Target ◽  
Deployment Planning ◽  
Directional Sensor Network ◽  
Directional Sensor

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

An Evolutionary Clustering-Based Optimization to Minimize Total Weighted Completion Time Variance in a Multiple Machine Manufacturing System

2015 ◽  
Vol 14 (05) ◽  
pp. 971-991 ◽  
Author(s):  
Hadi Mokhtari ◽  
Ali Salmasnia
Keyword(s):  
Manufacturing System ◽  
Search Strategy ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Total Weighted Completion Time ◽  
New Paradigm ◽  
Evolutionary Clustering ◽  
Processing Times ◽  
Proposed Model ◽  
Neighborhood Search Algorithm

This paper discusses clustering as a new paradigm of optimization and devises an integration of clustering and an evolutionary algorithm, neighborhood search algorithm (NSA), for a multiple machine system with the case of reducible processing times (RPT). After the problem is formulated mathematically, evolutionary clustering search (ECS) is devised to reach the near-optimal solutions. It is a way of detecting interesting search areas based on clustering. In this approach, an iterative clustering is carried out which is integrated to evolutionary mechanism NSA to identify which subspace is promising, and then the search strategy becomes more aggressive in detected areas. It is interesting to find out such subspaces as soon as possible to increase the algorithm's efficiency by changing the search strategy over possible promising regions. Once relevant search regions are discovered by clustering they can be treated with special intensification by the NSA algorithm. Furthermore, different neighborhood mechanisms are designed to be embedded within the main NSA algorithm so as to enhance its performance. The applicability of the proposed model and the performance of the NSA approach are demonstrated via computational experiments.

scholarly journals Knee Point Search Using Cascading Top-kSorting with Minimized Time Complexity

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zheng Wang ◽  
Shian-Shyong Tseng
Keyword(s):  
Time Complexity ◽  
Optimization Problem ◽  
Search Algorithm ◽  
A Priori ◽  
Search Problem ◽  
Time Cost ◽  
Point Distribution ◽  
Expected Time ◽  
Knee Point ◽  
Point Search

Anomaly detection systems and many other applications are frequently confronted with the problem of finding the largest knee point in the sorted curve for a set of unsorted points. This paper proposes an efficient knee point search algorithm with minimized time complexity using the cascading top-ksorting when a priori probability distribution of the knee point is known. First, a top-ksort algorithm is proposed based on a quicksort variation. We divide the knee point search problem into multiple steps. And in each step an optimization problem of the selection numberkis solved, where the objective function is defined as the expected time cost. Because the expected time cost in one step is dependent on that of the afterwards steps, we simplify the optimization problem by minimizing the maximum expected time cost. The posterior probability of the largest knee point distribution and the other parameters are updated before solving the optimization problem in each step. An example of source detection of DNS DoS flooding attacks is provided to illustrate the applications of the proposed algorithm.

scholarly journals SETI: The Microwave Search Problem and the Nasa Sky Survey Approach

1985 ◽  
Vol 112 ◽  
pp. 397-403
Author(s):  
Michael J. Klein ◽  
Samuel Gulkis
Keyword(s):  
Search Strategy ◽  
High Sensitivity ◽  
Search Problem ◽  
Dual Mode ◽  
Ongoing Work ◽  
Sky Survey ◽  
Frequency Coverage ◽  
Complementary Nature ◽  
Low Sensitivity ◽  
Survey Approach

NASA's microwave observing program for SETI is presented. This strategy is composed of a high sensitivity, narrow frequency coverage, Target Search and a low sensitivity, broad frequency coverage, Sky Survey. The complementary nature of this dual mode search strategy is discussed. An overview is given of ongoing work in the development of the search strategy for the Sky Survey.

Sign in / Sign up

Export Citation Format

Share Document