scholarly journals METODE STEEPEST DESCENT DENGAN UKURAN LANGKAH BARU UNTUK PENGOPTIMUMAN NIRKENDALA

2015 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
D. WUNGGULI ◽  
B. P. SILALAHI ◽  
S. GURITMAN
Keyword(s):  
Steepest Descent ◽  
Running Time

Metode steepest descent adalah metode gradien sederhana untuk pengoptimuman. Metode ini memiliki kekonvergenan yang lambat dalam menuju ke solusi optimum, hal ini terjadi karena langkahnya yang berbentuk zig-zag. Barzilai dan Borwein berusaha menyempurnakan metode ini dengan memodifikasi algoritme sehingga hasilnya berjalan cukup baik untuk masalah dengan dimensi yang besar. Hasil metode Barzilai dan Borwein ini telah memicu banyak penelitian pada metode steepest descent, diantaranya terdapat metode Alternatif Minimisasi dan metode Yuan. Dalam tulisan ini telah dimodifikasi metode steepest descent dengan ukuran langkah baru. Hasil modifikasi ini kemudian dibandingkan dengan metode Barzilai dan Borwein, Alternatif Minimisasi dan metode Yuan dengan kasus fungsi kuadratik ditinjau dari iterasi dan running time. Rata-rata hasil perbandingan menunjukkan bahwa modifikasi dengan ukuran langkah baru ini memberikan hasil yang baik untuk dimensi yang kecil dan mampu menyaingi hasil metode Barzilai-Borwein dan metode Alternatif Minimisasi untuk dimensi yang besar. Ukuran langkah baru ini memiliki kekonvergenan yang lebih cepat dibandingkan dengan m

scholarly journals PENYELARASAN ARAH VEKTOR GRADIEN UNTUK MENENTUKAN STEP SIZE METODE STEEPEST DESCENT PADA FUNGSI NONLINEAR KUADRATIK BANYAK VARIABEL

2018 ◽  
Vol 17 (1) ◽  
pp. 47
Author(s):  
S. IDAMAN ◽  
B. P. SILALAHI ◽  
S. GURITMAN
Keyword(s):  
Steepest Descent ◽  
Step Size ◽  
Running Time

Masalah optimisasi banyak variabel dapat diselesaikan dengan berbagai metode untuk mendapatkan solusi yang optimal. Salah satu metode yang paling sederhana yaitu metode <em>steepest descent</em>. Metode <em>steepest descent</em> menggunakan vektor gradien untuk menentukan arah pencarian disetiap iterasi kemudian ditentukan <em>step size</em> sebagai jarak perubahan solusi yang dipengaruhi oleh vektor gradien. <em>Step size </em>( ) pada metode <em>steepest descent</em> sangat mempengaruhi kecepatan kekonvergenan metode ini. Sehingga diperlukan penentuan <em>step size</em> yang tepat untuk mempercepat kekonvergenan metode <em>steepest descent</em>. Penelitian ini akan memodifikasi <em>step size</em> pada metode <em>st</em><em>eepest descent</em> dengan menentukan <em>step size</em> yang dapat menghasilkan arah (vektor gradien) yang mendekati vektor eigen dari matriks Heisse suatu fungsi kuadratik definit positif banyak variabel. Hasil numerik menunjukkan bahwa<em> step size </em>yang diperoleh pada penelitian ini bisa mereduksi jumlah iterasi dan<em> running time </em>lebih baik dari pada metode <em>steepest descent</em> biasa terutama untuk kasus <em>ill-conditioned </em>yaitu kasus lamanya metode <em>steepest descent</em> mencapai kekonvergenan yang disebabkan oleh perbandingan (rasio) yang besar antara nilai eigen terbesar dan nilai eigen terkecil dari matriks Heisse.

TEM Sample Prep Tool Matching—DualBeam FIB Cut Look and Measure Monitoring for Process Control and Development in a 300 mm IC Fab

2016 ◽  
Author(s):  
Jangbae Jeon
Keyword(s):  
Process Control ◽  
Sample Preparation ◽  
Continuous Improvement ◽  
Process Time ◽  
Operational Monitoring ◽  
Thickness Control ◽  
Running Time ◽  
End Products ◽  
Novel Method

Abstract This work presents a novel method of continuous improvement for faster, better and cheaper TEM sample preparation using Cut Look and Measure (CLM). The improvement of the process is executed by operational monitoring of daily beam conditions, end products, bulk thickness control, recipe usage and tool running time. This process produces a consequent decrease in rework rate and process time. In addition, it also increases throughput with better quality TEM samples.

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

Ultrashort pulses propagation through different approaches of the Split-Step Fourier method

2018 ◽  
Vol 1 (3) ◽  
pp. 2
Author(s):  
José Stênio De Negreiros Júnior ◽  
Daniel Do Nascimento e Sá Cavalcante ◽  
Jermana Lopes de Moraes ◽  
Lucas Rodrigues Marcelino ◽  
Francisco Tadeu De Carvalho Belchior Magalhães ◽  
...  
Keyword(s):  
Energy Conservation ◽  
Optical Pulse ◽  
Time Window ◽  
Fourier Method ◽  
Ultrashort Pulses ◽  
Nonlinear Effects ◽  
Single Mode ◽  
Time Algorithm ◽  
Optical Pulses ◽  
Running Time

Simulating the propagation of optical pulses in a single mode optical fiber is of fundamental importance for studying the several effects that may occur within such medium when it is under some linear and nonlinear effects. In this work, we simulate it by implementing the nonlinear Schrödinger equation using the Split-Step Fourier method in some of its approaches. Then, we compare their running time, algorithm complexity and accuracy regarding energy conservation of the optical pulse. We note that the method is simple to implement and presents good results of energy conservation, besides low temporal cost. We observe a greater precision for the symmetrized approach, although its running time can be up to 126% higher than the other approaches, depending on the parameters set. We conclude that the time window must be adjusted for each length of propagation in the fiber, so that the error regarding energy conservation during propagation can be reduced.

scholarly journals Faster Motif Counting via Succinct Color Coding and Adaptive Sampling

2021 ◽  
Vol 15 (6) ◽  
pp. 1-27
Author(s):  
Marco Bressan ◽  
Stefano Leucci ◽  
Alessandro Panconesi
Keyword(s):  
Adaptive Sampling ◽  
Relative Frequency ◽  
State Of The Art ◽  
Color Coding ◽  
Input Graph ◽  
Large Graphs ◽  
Running Time ◽  
Uniform Sampling ◽  
Current State ◽  
Connected Subgraphs

We address the problem of computing the distribution of induced connected subgraphs, aka graphlets or motifs , in large graphs. The current state-of-the-art algorithms estimate the motif counts via uniform sampling by leveraging the color coding technique by Alon, Yuster, and Zwick. In this work, we extend the applicability of this approach by introducing a set of algorithmic optimizations and techniques that reduce the running time and space usage of color coding and improve the accuracy of the counts. To this end, we first show how to optimize color coding to efficiently build a compact table of a representative subsample of all graphlets in the input graph. For 8-node motifs, we can build such a table in one hour for a graph with 65M nodes and 1.8B edges, which is times larger than the state of the art. We then introduce a novel adaptive sampling scheme that breaks the “additive error barrier” of uniform sampling, guaranteeing multiplicative approximations instead of just additive ones. This allows us to count not only the most frequent motifs, but also extremely rare ones. For instance, on one graph we accurately count nearly 10.000 distinct 8-node motifs whose relative frequency is so small that uniform sampling would literally take centuries to find them. Our results show that color coding is still the most promising approach to scalable motif counting.

scholarly journals A Comparative Study among New Hybrid Root Finding Algorithms and Traditional Methods

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1306
Author(s):  
Elsayed Badr ◽  
Sultan Almotairi ◽  
Abdallah El Ghamry
Keyword(s):  
Comparative Study ◽  
Numerical Results ◽  
Traditional Methods ◽  
Running Time ◽  
Root Finding ◽  
Average Running Time ◽  
False Position ◽  
Newton Raphson ◽  
Number Of Iterations

In this paper, we propose a novel blended algorithm that has the advantages of the trisection method and the false position method. Numerical results indicate that the proposed algorithm outperforms the secant, the trisection, the Newton–Raphson, the bisection and the regula falsi methods, as well as the hybrid of the last two methods proposed by Sabharwal, with regard to the number of iterations and the average running time.

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