Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Random Sampling Method01:09

Random Sampling Method

15.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.6K
Aliasing01:18

Aliasing

765
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
765
Upsampling01:22

Upsampling

692
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
692
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

824
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
824
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

393
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
393
Sampling Theorem01:15

Sampling Theorem

1.5K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interlayer co-chemistry of a homologous ion stabilizer and microenvironmental molecular regulator for high-performance zinc-ion storage.

Chemical science·2026
Same author

Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models.

Bioinformatics (Oxford, England)·2025
Same author

Plate-like Multiprincipal Cation Ceramic Powders with Aurivillius and Perovskite Structures Fabricated by Molten Salt Synthesis.

Inorganic chemistry·2025
Same author

Two-State Stochastic Model of In Vivo Observations of Transcriptional Bursts.

Brazilian journal of physics·2025
Same author

Formation of Corrugated Damage on Bearing Race under Different AC Shaft Voltages.

Materials (Basel, Switzerland)·2024
Same author

Evolution of biological cooperation: an algorithmic approach.

Scientific reports·2024
Same journal

Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking.

Parallel computing·2021
Same journal

Multiscale modeling and cinematic visualization of photosynthetic energy conversion processes from electronic to cell scales.

Parallel computing·2021
Same journal

Asynchronous Parallel Stochastic Quasi-Newton Methods.

Parallel computing·2020
Same journal

A global perspective of atmospheric carbon dioxide concentrations.

Parallel computing·2020
Same journal

Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms.

Parallel computing·2017
Same journal

Atomic Detail Visualization of Photosynthetic Membranes with GPU-Accelerated Ray Tracing.

Parallel computing·2016
See all related articles

Related Experiment Video

Updated: Mar 24, 2026

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

2.3K

Parallel Simulated Annealing Using an Adaptive Resampling Interval.

Zhihao Lou1, John Reinitz2

  • 1Department of Computer Science, the University of Chicago, Chicago, Illinois, USA.

Parallel Computing
|March 5, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel parallel simulated annealing algorithm that significantly enhances scalability for complex optimization problems. It achieves high parallel efficiency by periodically resampling states across processors.

Keywords:
MPIRastrigin functionevolutionary computationglobal optimizationparallel algorithmstochastic optimization

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

2.1K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K

Related Experiment Videos

Last Updated: Mar 24, 2026

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

2.3K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

2.1K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K

Area of Science:

  • Computational Science
  • Optimization Algorithms
  • Parallel Computing

Background:

  • The method by Chu et al. (1999) faced scalability limitations in parallel simulated annealing.
  • Adaptive cooling based on variance hindered performance in previous parallel approaches.
  • High-dimensional optimization problems require efficient parallel algorithms.

Purpose of the Study:

  • To develop a parallel simulated annealing algorithm that overcomes scalability barriers.
  • To improve parallel efficiency in terms of both iteration and time for complex functions.
  • To introduce an adaptive resampling strategy for enhanced performance.

Main Methods:

  • A parallel simulated annealing algorithm was designed, abandoning adaptive cooling based on variance.
  • Periodic resampling of states across processors was implemented.
  • An adaptive method for tuning the resampling interval based on adoption rate was developed.

Main Results:

  • The algorithm achieved 90% parallel efficiency in iteration and 40% in time on a 5000-dimension Rastrigin function using up to 192 processors.
  • Abandoning adaptive cooling led to substantial gains in parallel efficiency.
  • The adaptive resampling method yielded nearly identical parallel efficiency with higher success rates compared to fixed intervals.

Conclusions:

  • The proposed parallel simulated annealing algorithm offers superior scalability and efficiency for high-dimensional optimization.
  • The adaptive resampling strategy effectively optimizes performance across different processor configurations.
  • This work presents a significant advancement in parallel optimization techniques.