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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
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...
88
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

110
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
110
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

458
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
458
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

647
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
647
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

457
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
457
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

120
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
120

You might also read

Related Articles

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

Sort by
Same authorSame journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same author

Laplacian spectrum constrains collective performance enhancement.

Physical review. E·2026
Same author

Coexistence of many positive invariant sets in several classes of dynamical systems.

Chaos (Woodbury, N.Y.)·2026
Same author

Fuzzy reinforcement learning synchronization of stochastic dynamic networks: An adaptive event-triggered strategy.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Event-triggered pinning synchronization of stochastic complex networks under hybrid attacks.

Chaos (Woodbury, N.Y.)·2026
Same author

Prognostic Nutritional Index and Cancer Prognostic Outcomes: An Umbrella Review of Systematic Reviews and Meta-analyses of Observational Studies.

Advances in nutrition (Bethesda, Md.)·2026

Related Experiment Video

Updated: Jul 31, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Distributed Discrete-Time Convex Optimization With Closed Convex Set Constraints: Linearly Convergent Algorithm

Meng Luan, Guanghui Wen, Hongzhe Liu

    IEEE Transactions on Cybernetics
    |May 9, 2023
    PubMed
    Summary

    New distributed optimization algorithms achieve fast convergence for convex problems over directed networks. These algorithms offer linear speedup, enhancing practical applications in distributed systems.

    More Related Videos

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.7K
    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    1.7K

    Related Experiment Videos

    Last Updated: Jul 31, 2025

    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
    10:58

    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

    Published on: July 25, 2013

    17.1K
    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.7K
    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    1.7K

    Area of Science:

    • Distributed optimization
    • Networked systems
    • Convex analysis

    Background:

    • Practical distributed optimization requires efficient convergence and applicability to various network structures.
    • Directed interaction topologies present unique challenges for algorithm design and analysis.
    • Existing algorithms may not fully exploit network properties for accelerated performance.

    Purpose of the Study:

    • To develop novel, fast distributed discrete-time algorithms for convex optimization over directed graphs.
    • To address the challenges posed by directed interaction topologies in networked systems.
    • To achieve linear speedup convergence rates for enhanced practical applicability.

    Main Methods:

    • Designed two distributed algorithms under a gradient tracking framework, tailored for balanced and unbalanced directed graphs.
    • Incorporated momentum terms and two time-scales to accelerate convergence.
    • Analyzed algorithm performance theoretically, focusing on convergence rates.

    Main Results:

    • Demonstrated that the proposed distributed algorithms achieve linear speedup convergence rates.
    • Showcased the effectiveness of the algorithms on both balanced and unbalanced directed graphs.
    • Verified the global accelerated effect through numerical simulations.

    Conclusions:

    • The developed distributed algorithms are effective for solving convex optimization problems over directed networks.
    • The algorithms offer significant improvements in convergence speed, achieving linear speedup.
    • The findings are crucial for advancing practical applications of distributed optimization in complex network environments.