Research

I have broad optimization and machine learning interests. In my PhD, I mostly studied stability and sensitivity analysis in multi-objective optimization.  After my PhD, I have been doing research on the following topics:

•         Complexity of first order methods in machine learning

•         Non-convex quadratic programming

•         Absolute value equations

 Complexity of first order methods in machine learning

• Zamani, M., Glineur, F., & Hendrickx, J. M. (2024). On the Set of Possible Minimizers of a Sum of Convex Functions. arXiv preprint arXiv:2403.05467. 

• Zamani, M., Abbaszadehpeivasti, H., & de Klerk, E. (2022). The exact worst-case convergence rate of the alternating direction method of multipliers.  Mathematical Programming. 

• Zamani, M., & Glineur, F. (2023). Exact convergence rate of the last iterate in subgradient methods. arXiv preprint arXiv:2307.11134.

• Zamani, M., Abbaszadehpeivasti, H., & de Klerk, E. (2022). Convergence rate analysis of the gradient descent-ascent method for convex-concave saddle-point problems. arXiv: submit/4477485.

• Abbaszadehpeivasti, H., de Klerk, E., & Zamani, M. (2023). Conditions for linear convergence of the gradient method for non-convex optimization. Optimization Letters, 17(5), 1105-1125.

• Abbaszadehpeivasti, H., de Klerk, E., & Zamani, M. (2023). On the rate of convergence of the difference-of-convex algorithm (DCA). Journal of Optimization Theory and Applications, 1-22.

• Abbaszadehpeivasti, H., de Klerk, E., & Zamani, M. (2022). The exact worst-case convergence rate of the gradient method with fixed step lengths for L-smooth functions. Optimization Letters, 16(6), 1649-1661. 

  Absolute value equations

• Zamani, M., & Hladík, M. (2022). Error bounds and a condition number for the absolute value equations. Mathematical Programming, 1-29. 

• Zamani, M., & Hladík, M. (2021). A new concave minimization algorithm for the absolute value equation solution. Optimization Letters, 15(6), 2241-2254. 

• Hladíc, M & Zamani, M. (2023)  Absolute Value Programming. Encyclopedia of Optimization.

  Non-convex quadratic programming

• Zamani, M. (2019). A new algorithm for concave quadratic programming. Journal of Global Optimization, 75(3), 655-681. 

• Zamani, M. (2022). New bounds for nonconvex quadratically constrained quadratic programming. Journal of Global Optimization. 

• Hladík, M., Hartman, D., & Zamani, M. (2021). Maximization of a PSD quadratic form and factorization. Optimization Letters, 15(7), 2515-2528. 

  Multi-objective optimization

• Luc, D. T., Soleimani-Damaneh, M., & Zamani, M. (2018). Semi-differentiability of the marginal mapping in vector optimization. SIAM Journal on Optimization, 28(2), 1255-1281. 

• Zamani, M., Soleimani-Damaneh, M., & Kabgani, A. (2015). Robustness in nonsmooth nonlinear multi-objective programming. European Journal of Operational Research, 247(2), 370-378. 

• Zamani, M., & Soleimani-damaneh, M. (2022). Proper efficiency, scalarization and transformation in multi-objective optimization: unified approaches. Optimization, 71(3), 753-774. 

• Soleimani-Damaneh, M., & Zamani, M. (2016). On Benson’s scalarization in multiobjective optimization. Optimization Letters, 10(8), 1757-1762. 

• Soleimani–Damaneh, M., & Zamani, M. (2018). On compromise solutions in multiple objective programming. RAIRO-Operations Research, 52(2), 383-390. 

• PhD Thesis: Zamani, M. (2016, July). Scalarization and stability in multi-objective optimization. Avignon. 

Non-smooth optimization

• Kabgani, A., Soleimani-Damaneh, M., & Zamani, M. (2017). Optimality conditions in optimization problems with convex feasible set using convexificators. Mathematical Methods of Operations Research, 86(1), 103-121.