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

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 Problems01:26

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...
Midpoint Rule01:20

Midpoint Rule

Approximating areas under curved boundaries is a common problem in applied mathematics, particularly when an exact calculation is difficult or impractical. One effective numerical method for this purpose is the Midpoint Rule, which provides an estimate of the area under a curve by using rectangular approximations over a specified interval.Description of the Midpoint RuleThe Midpoint Rule begins by dividing the given interval into a number of equal subintervals. For each subinterval, the...
Approximate Integration01:24

Approximate Integration

In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
Critical Numbers and the Closed Interval Method01:21

Critical Numbers and the Closed Interval Method

Understanding the maximum and minimum values of a function is essential for analyzing its overall behavior. These values, often referred to as extrema, provide insight into how a function behaves across its domain. In mathematical terms, extrema can be either local—representing peaks and valleys within a limited region—or absolute, indicating the highest or lowest points over an entire interval.A function’s extrema occur at critical numbers, which are values in the domain where the derivative...
Area Problem01:26

Area Problem

Determining the area of a region with straight edges is straightforward, as geometric formulas for rectangles, triangles, and polygons can be applied directly. However, traditional geometric methods are insufficient when a region has a curved boundary, such as the area under a function.fromThe area problem involves finding a systematic way to measure such regions. One approach to solving this problem is through approximation. Instead of attempting to compute the area exactly at the outset, the...

You might also read

Related Articles

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

Sort by
Same author

Integrated analysis of gut microbiota, serum metabolomics, and proteomics reveals novel associations with clinical symptoms in patients with cerebral infarction.

BMC microbiology·2026
Same author

Mining Association Patterns From Neighborhood Insight.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

SCGT: Towards Scalable and Comprehensive Graph Transformer.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Preformer MOT: A transformer-based approach for multi-object tracking with global trajectory prediction.

Fundamental research·2026
Same author

Graph-Enhanced Visual Prompting for Pre-Trained Models Adaptation in Medical Imaging Classification.

IEEE transactions on medical imaging·2026
Same author

Physiological, Biochemical and Gene Expression Analyses of <i>Halimodendron halodendron</i> Responding to Drought Stress.

Genes·2025
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A deterministic annealing algorithm for approximating a solution of the min-bisection problem.

Chuangyin Dang1, Wei Ma, Jiye Liang

  • 1Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong. mecdang@cityu.edu.hk

Neural Networks : the Official Journal of the International Neural Network Society
|November 11, 2008
PubMed
Summary
This summary is machine-generated.

This study presents a new algorithm for the NP-hard min-bisection problem, offering an efficient approach to find approximate solutions. Numerical results demonstrate its superior performance over existing heuristic methods.

Related Experiment Videos

Last Updated: Jun 28, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Combinatorial Optimization
  • Continuous Optimization

Background:

  • The min-bisection problem is a computationally challenging NP-hard problem.
  • Existing heuristic methods like MSKL and MLGP have limitations in efficiency.

Purpose of the Study:

  • To formulate an equivalent linearly constrained continuous optimization problem for min-bisection.
  • To propose a novel algorithm for approximating the solution to this continuous problem.

Main Methods:

  • Formulation of an equivalent continuous optimization problem.
  • Development of an algorithm using a logarithmic-cosine barrier function and an annealing procedure.
  • Utilizing a feasible descent direction with automatic bound satisfaction.

Main Results:

  • The algorithm is proven to converge to at least a local minimum.
  • Numerical results indicate significantly higher efficiency compared to MSKL and MLGP.
  • The proposed method demonstrates effectiveness in approximating solutions for the min-bisection problem.

Conclusions:

  • The developed algorithm provides an efficient and effective method for solving the min-bisection problem.
  • This approach offers a promising alternative to existing heuristic techniques.
  • The continuous optimization formulation simplifies the handling of constraints.