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

Application of Linearization and Approximation01:29

Application of Linearization and Approximation

181
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
181
Methods of Medium Optimization01:28

Methods of Medium Optimization

49
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
49
Linearization and Approximation01:26

Linearization and Approximation

190
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
190
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

420
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,...
420
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

407
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...
407
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

783
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
783

You might also read

Related Articles

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

Sort by
Same author

Asymmetric polydopamine-coated polyacrylic acid-ammonia borane nanoparticles as a hydrogen storage material for synergistic hydrogen and photothermal therapy.

Journal of colloid and interface science·2026
Same author

${\mathcal{L}}_{\infty}$ Control of Switched T-S Fuzzy Systems Under Relieved Asynchronous Switching: A Zonotope Analysis Strategy.

IEEE transactions on cybernetics·2026
Same author

Dynamic Gain-Driven Adaptive Quantized Output Feedback Control for Nonlinear Systems Governed by Parameter Criteria.

IEEE transactions on cybernetics·2026
Same author

Ramulus Mori (Sangzhi) Alkaloids (SZ-A) exert a renoprotective effect by activating PGC-1α to mediate fatty acid oxidation and mitochondrial homeostasis.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Secondhand smoke exposure and sleep disturbances among Korean adolescents: A nationally representative cross-sectional study.

Tobacco induced diseases·2026
Same author

Learning-based minimum cost strategies for set reachability of Boolean control networks under data injection attacks.

Neural networks : the official journal of the International Neural Network Society·2025
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: Mar 31, 2026

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

13.6K

Zeroth-Order Method for Distributed Optimization With Approximate Projections.

Deming Yuan, Daniel W C Ho, Shengyuan Xu

    IEEE Transactions on Neural Networks and Learning Systems
    |October 16, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a distributed zeroth-order method for minimizing convex functions across networks, even without gradient information or exact projections. The method ensures convergence to optimal values with decreasing approximation errors.

    Related Experiment Videos

    Last Updated: Mar 31, 2026

    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

    13.6K

    Area of Science:

    • Optimization
    • Distributed Systems
    • Network Science

    Background:

    • Minimizing sums of convex functions is crucial in distributed systems.
    • Handling nonsmooth functions and state constraints presents computational challenges.
    • Network structures and information availability impact algorithm design.

    Purpose of the Study:

    • To develop a distributed zeroth-order optimization method for time-varying directed networks.
    • To address computational constraints including lack of gradient information and approximate projections.
    • To analyze the convergence properties of the proposed method.

    Main Methods:

    • Devised a distributed zeroth-order algorithm.
    • Utilized only functional evaluations and approximate projection steps.
    • Analyzed convergence under specific error reduction rates.

    Main Results:

    • The proposed method converges to the optimal value.
    • Convergence is achieved despite nonsmooth functions and network complexities.
    • Demonstrated the effectiveness of functional evaluations and approximate projections.

    Conclusions:

    • The developed distributed zeroth-order method is effective for constrained convex optimization in networks.
    • The method offers a viable solution when gradient information is unavailable or projections are approximate.
    • Future work can explore variations in network topology and function types.