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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

328
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
328
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

228
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...
228
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.0K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.0K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.1K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.1K
Classification of Systems-II01:31

Classification of Systems-II

427
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
427
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

439
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
439

You might also read

Related Articles

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

Sort by
Same author

Genome-wide characterization and expression profiling of the NAC genes under abiotic stresses in Cucumis sativus.

Plant physiology and biochemistry : PPB·2017
Same author

Comparing performance of Bonfils fiberscope and GlideScope videolaryngoscope for awake intubation.

Journal of clinical anesthesia·2017
Same author

Use of dual priming oligonucleotide system-based multiplex RT-PCR combined with high performance liquid chromatography assay for simultaneous detection of five enteric viruses associated with acute enteritis.

Journal of virological methods·2017
Same author

Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing.

Sensors (Basel, Switzerland)·2017
Same author

Actein inhibits glioma growth via a mitochondria-mediated pathway.

Cancer biomarkers : section A of Disease markers·2017
Same author

MitoQ regulates autophagy by inducing a pseudo-mitochondrial membrane potential.

Autophagy·2017
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Dec 23, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.3K

Distributed Optimization for Two Types of Heterogeneous Multiagent Systems.

Chao Sun, Maojiao Ye, Guoqiang Hu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 21, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study develops distributed optimization algorithms for heterogeneous multiagent systems. Algorithms ensure agents reach optimal solutions despite differing dynamics and objective functions.

    More Related Videos

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.7K

    Related Experiment Videos

    Last Updated: Dec 23, 2025

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.3K
    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.7K

    Area of Science:

    • Control Theory
    • Distributed Systems
    • Optimization

    Background:

    • Heterogeneous multiagent systems present unique coordination challenges.
    • Distributed optimization is crucial for large-scale systems with local objectives.

    Purpose of the Study:

    • To develop and analyze distributed optimization algorithms for systems with mixed continuous-time and discrete-time agents.
    • To investigate convergence properties for systems with mixed first-order and second-order agents.

    Main Methods:

    • A distributed subgradient method is proposed for agents with mixed dynamics.
    • Convergence analysis is performed under specific conditions on subgradients, step sizes, and sampling periods.
    • Analysis for mixed-order agents considers strong convexity and Lipschitz continuity of objective functions.

    Main Results:

    • Convergence to an optimal solution is proven for systems with mixed continuous-time and discrete-time agents under bounded subgradients and specific step-size rules.
    • Convergence to a unique optimal solution is demonstrated for systems with mixed first-order and second-order agents under strong convexity and Lipschitz continuity.
    • Numerical examples validate the theoretical findings.

    Conclusions:

    • The proposed distributed optimization algorithms are effective for heterogeneous multiagent systems.
    • The methods ensure convergence to optimal solutions in diverse agent configurations.
    • This work advances the theory and application of distributed optimization in complex systems.