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

Radical Chain-Growth Polymerization: Chain Branching01:17

Radical Chain-Growth Polymerization: Chain Branching

1.9K
The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
1.9K
Radical Chain-Growth Polymerization: Mechanism01:09

Radical Chain-Growth Polymerization: Mechanism

2.4K
The radical chain-growth polymerization mechanism consists of three steps: initiation, propagation, and termination of polymerization. The polymerization initiates when a free radical generated from the radical initiator adds to the unsaturated bond in the monomer. The unpaired electron of the free radical and one π electron in the unsaturated bond creates a σ bond between the free radical and the monomer. As a result, the other π electron in the unsaturated bond converts this...
2.4K
Radical Chain-Growth Polymerization: Overview01:10

Radical Chain-Growth Polymerization: Overview

2.3K
Chain-growth or addition polymerization is successive addition reactions of monomers with a polymer chain. In radical chain-growth polymerization, the reaction proceeds via a free-radical intermediate. The free radical is formed from radical initiators, which spontaneously generate free radicals by homolytic fission. Organic peroxides (such as dibenzoyl peroxide, as shown in Figure 1) or azo compounds are popular radical initiators. A low concentration ratio of radical initiator to monomer is...
2.3K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

31.0K
sp3d and sp3d 2 Hybridization
31.0K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

45.5K
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
45.5K
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

3.2K
Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Optimized fetal head circumference estimation in 2D ultrasound using EfficientNet-B7 and Adam optimizer.

BMC pediatrics·2026
Same author

Integrative framework for cancer detection via integro-differential equations using deep learning techniques.

Scientific reports·2026
Same author

Deep Learning-Driven Early Diagnosis of Respiratory Diseases using CNN-RNN Fusion on Lung Sound Data.

Scientific reports·2025
Same author

Explainable deep reinforcement learning for climate forecasting with transfer learning.

Environmental science and pollution research international·2025
Same author

MSRP-TODNet: a multi-scale reinforced region wise analyser for tiny object detection.

BMC research notes·2025
Same author

A novel optimization-driven deep learning framework for the detection of DDoS attacks.

Scientific reports·2024
Same journal

The Outcome of Cardiac Hydatid Surgery in The Iraqi Center of Heart Diseases.

F1000Research·2026
Same journal

Perception of body donation among the Phase-1 medical students, a questionnaire-based study.

F1000Research·2026
Same journal

Exploring Infertility in Saudi Arabia: Qualitative Insights into IVF Treatment Services and Policy Recommendations.

F1000Research·2026
Same journal

Cyber Military Operations under International Humanitarian Law: Interpreting the Concept of "Attack" and Challenges in Protecting Civilians.

F1000Research·2026
Same journal

Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model.

F1000Research·2026
Same journal

Emotional intelligence: An important skill to learn now more than ever.

F1000Research·2026
See all related articles

Related Experiment Video

Updated: May 11, 2025

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

513

Hybrid optimization technique for matrix chain multiplication using Strassen's algorithm.

Srinivasarao Thota1, Thulasi Bikku2, Rakshitha T3

  • 1Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India.

F1000Research
|April 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid Matrix Chain Multiplication (MCM) method combining dynamic programming with Strassen's algorithm. The optimized MCM significantly speeds up large matrix computations, reducing execution time and memory usage.

Keywords:
Computational Complexity.Dynamic ProgrammingHybrid OptimizationMatrix Chain MultiplicationStrassen’s Algorithm

More Related Videos

Novel and Innovative Hybrid Technique for Type A Aortic Dissection
06:26

Novel and Innovative Hybrid Technique for Type A Aortic Dissection

Published on: March 28, 2025

145
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

320

Related Experiment Videos

Last Updated: May 11, 2025

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

513
Novel and Innovative Hybrid Technique for Type A Aortic Dissection
06:26

Novel and Innovative Hybrid Technique for Type A Aortic Dissection

Published on: March 28, 2025

145
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

320

Area of Science:

  • Computational Mathematics
  • Computer Science
  • Scientific Computing

Background:

  • Matrix Chain Multiplication (MCM) is crucial in scientific computing, graphics, and machine learning.
  • Traditional MCM uses Dynamic Programming (DP) with Memoization but suffers from O(n^3) complexity for large matrices.
  • Standard matrix multiplication is inefficient for large-scale computations.

Purpose of the Study:

  • To develop a hybrid optimization technique for Matrix Chain Multiplication.
  • To accelerate matrix multiplication by integrating Strassen's algorithm into MCM.
  • To reduce computational complexity and improve efficiency for large matrices.

Main Methods:

  • A two-phase approach: (i) optimizing matrix chain order with top-down DP and memoization, and (ii) a hybrid multiplication strategy.
  • Selective application of Strassen's algorithm (O(n^2.81)) for matrices with n ≥ 128.
  • Comparison with traditional MCM and standalone Strassen's algorithm via computational experiments.

Main Results:

  • The hybrid MCM method achieved significant speedups (4x-8x) compared to traditional methods.
  • Demonstrated reduction in memory consumption for large-scale applications.
  • Maintained numerical accuracy while improving performance.

Conclusions:

  • The proposed hybrid MCM approach effectively reduces execution time and memory usage.
  • Selective integration of Strassen's algorithm enhances MCM efficiency for large matrices.
  • Opens avenues for parallel computing and GPU acceleration in matrix operations.