Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Optimization Problems

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

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...

Lagrange Multipliers: Two Constraints

Lagrange Multipliers: Two Constraints

The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.

Gaussian Elimination: Problem Solving

Gaussian Elimination: Problem Solving

Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...

Lagrange Multipliers: Problem Solving

Lagrange Multipliers: Problem Solving

A silo with a cylindrical base, flat bottom, and hemispherical roof is a common design in agricultural and industrial storage due to its structural efficiency and ease of construction. Optimizing its dimensions to maximize storage capacity for a given amount of material—i.e., a fixed surface area—is a classic problem in applied calculus and engineering design. The key parameters are the radius r of the base and the height h of the cylindrical section.The total volume of the silo is obtained by...

You might also read

Related Articles

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

Sort by

Same author

Digital transplantation pathology: combining whole slide imaging, multiplex staining and automated image analysis.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2011

Same author

Robust, globally consistent and fully automatic multi-image registration and montage synthesis for 3-D multi-channel images.

Journal of microscopy·2011

Same author

Self-aligned Schwann cell monolayers demonstrate an inherent ability to direct neurite outgrowth.

Journal of neural engineering·2010

Same author

Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion.

Journal of neural engineering·2006

Same author

Topographically modified surfaces affect orientation and growth of hippocampal neurons.

Journal of neural engineering·2005

Same author

Genetics and imaging to assess oocyte and preimplantation embryo health.

Reproduction, fertility, and development·2005

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

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

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

Published on: December 9, 2012

Hierarchically structured unit-simplex transformations for parallel distributed optimization problems.

B Roysam¹, A K Bhattacharjya

¹Rensselaer Polytech. Inst., Troy, NY.

IEEE Transactions on Neural Networks

|January 1, 1992

Summary

This summary is machine-generated.

A new hierarchical deformable-template method ensures unit-simplex constraints are strictly met. This stable, deterministic approach enables multiresolution processing and efficient incorporation of global constraints for large-scale applications.

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Published on: December 9, 2012

Area of Science:

Computational mathematics
Optimization algorithms
Machine learning

Background:

Unit-simplex constraints are crucial in various fields, including probability and statistics.
Existing methods for handling these constraints can be computationally intensive or introduce unwanted limitations.
Hierarchical structures offer potential for more efficient and flexible constraint satisfaction.

Purpose of the Study:

To present a stable deterministic approach for incorporating unit-simplex constraints.
To develop a method based on a hierarchical deformable-template structure.
To demonstrate the applicability and advantages of the proposed method.

Main Methods:

A hierarchical deformable-template structure is utilized.
The approach guarantees strict confinement to the unit-simplex constraint set.
It allows for multiresolution processing and incorporation of global constraints like recursive symmetries.

Main Results:

The method ensures strict adherence to unit-simplex constraints without introducing extraneous ones.
A hierarchical network interconnection is achieved, differing from global structures.
Efficient closed-form incorporation of global constraints, such as recursive symmetries, is demonstrated.

Conclusions:

The presented hierarchical deformable-template approach offers a stable and deterministic solution for unit-simplex constrained optimization.
This method facilitates multiresolution analysis and integrates global constraints effectively.
The approach is suitable for large-scale applications, as evidenced by illustrative examples.