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Related Experiment Video

Updated: Jan 27, 2026

Generation of Murine Cardiac Pacemaker Cell Aggregates Based on ES-Cell-Programming in Combination with Myh6-Promoter-Selection
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Numerical solution of a general interval quadratic programming model for portfolio selection.

Jianjian Wang1, Feng He1, Xin Shi2,3,4

  • 1Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing, Peoples R China.

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|March 14, 2019
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Summary

This study introduces a new interval quadratic programming model for portfolio selection, incorporating transaction costs and market liquidity. The proposed method offers a more realistic and feasible approach to optimizing investment portfolios under uncertainty.

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Area of Science:

  • Finance
  • Operations Research
  • Mathematical Optimization

Background:

  • Traditional portfolio selection models, like the Markowitz mean-variance model, often simplify real-world market conditions.
  • Uncertainty in financial markets necessitates more robust portfolio optimization strategies.
  • Factors such as transaction costs and market liquidity significantly impact portfolio performance.

Purpose of the Study:

  • To develop a more realistic and optimized portfolio selection model.
  • To address the limitations of existing models by incorporating transaction costs and liquidity.
  • To propose an effective numerical solution for the new model.

Main Methods:

  • Development of a general interval quadratic programming model for portfolio selection.
  • Introduction of linear transaction costs and market liquidity into the model.
  • Application of Lagrange theorem and duality theory for numerical solution.
  • Estimation of objective function bounds using the proposed method.

Main Results:

  • The proposed interval quadratic programming model provides a more realistic framework for portfolio selection.
  • The numerical solution method effectively estimates the upper and lower bounds of the objective function.
  • Illustrative examples demonstrate the model's feasibility and effectiveness.
  • The new method outperforms commonly used portfolio selection techniques.

Conclusions:

  • The developed interval quadratic programming model enhances portfolio selection by accounting for transaction costs and liquidity.
  • The proposed numerical solution is effective and feasible for practical application.
  • This research offers an improved approach to portfolio optimization in uncertain financial environments.