Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
December 19, 2025journal of the college of basic educationOpen Access

An Effective of Numerical Global Optimization Framework For ODE-Constrained Problems

View Full Paper
Ask AI
Bookmark
Share

Authors

MAMourad Abed

Discussion

Loading...

Member takes

Overview

Innovative framework improves convergence in non-convex ODE-constrained problems, suggesting enhanced optimization methods.

Key Points

  • To develop an effective framework for optimizing non-convex ordinary differential equation constrained problems.
  • Utilized convex relaxation techniques
  • Implemented a deterministic spatial Branch and Bound algorithm
  • Employed adaptive branching strategies
  • Updated upper and lower bounds using sub- and super-function concepts
  • Framework demonstrates effective convergence to global optimum
  • Numerical case studies validated the framework's performance

Cite This Study

Mourad Abed (2025) studied this question.

synapsesocial.com/papers/69449a892f0218eca9508304https://doi.org/10.35950/cbej.v30i133.13986
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Enhancing Inner Linearizations Assisted by Gradient-Based Expansion Point Optimization2026
  2. 2Computing subgradients of convex relaxations for solutions of parametric ordinary differential equations2024 · 4 citations
  3. 3A branch-and-bound algorithm with growing datasets for large-scale parameter estimation2024 · 7 citations
  4. 4An Efficient Optimization Approach for Solving Nonlinear Variable‐Order Fractional PDEs With Nonlocal Boundary Conditions2025
  5. 5Parameter estimation in ODEs: assessing the potential of local and global solvers2025 · 2 citations