JIE SUN

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Professor

Curtin University

Australia

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About

Jie Sun is a Professor at Curtin University in Australia. His research focuses on optimization techniques and stochastic programming, as demonstrated by his recent publications on topics such as multi-stage pseudomonotone variational inequalities and augmented Lagrangian methods. He employs advanced algorithms to address complex problems in financial models and optimization under uncertainty.

Recent Grants

Grant: Close

Some Computational Developments for Applications of Piecewise Quadratic Programs

Open Date: 1988-04-15

Close Date: 1991-03-31

Grant: Close

Risk and Reliability in Stochastic Optimisation and Equilibrium

Open Date:

Close Date:

Articles (16)

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2021

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2021

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2021

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2020

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2020

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2020

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2020

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2020

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2020

Collaborators (1)

Ling Li

Curtin University

AUSTRALIA

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JIE SUN

Research Interests

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About

Recent Grants

Articles (16)

A robust financing theory of ICOs under demand uncertainty of products of token platforms

A Mini-Batch Proximal Stochastic Recursive Gradient Algorithm with Diagonal Barzilai–Borwein Stepsize

A NOVEL AUGMENTED LAGRANGIAN METHOD OF MULTIPLIERS FOR OPTIMIZATION WITH GENERAL INEQUALITY CONSTRAINTS

AN INERTIAL TRIPLE-PROJECTION ALGORITHM FOR SOLVING THE SPLIT FEASIBILITY PROBLEM

On Multistage Pseudomonotone Stochastic Variational Inequalities

An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming

VARIABLE METRIC PROXIMAL STOCHASTIC VARIANCE REDUCED GRADIENT METHODS FOR NONCONVEX NONSMOOTH OPTIMIZATION

A Minibatch Proximal Stochastic Recursive Gradient Algorithm Using a Trust-Region-Like Scheme and Barzilai–Borwein Stepsizes

An Augmented Lagrangian Decomposition Method for Chance-Constrained Optimization Problems

A time-consistent Benders decomposition method for multistage distributionally robust stochastic optimization with a scenario tree structure

Mixed-integer minimax dynamic optimization for structure identification of glycerol metabolic network

Risk minimization, regret minimization and progressive hedging algorithms

Solving Lagrangian variational inequalities with applications to stochastic programming

A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm

A strategy of global convergence for the affine scaling algorithm for convex semidefinite programming

Spectral Operators of Matrices: Semismoothness and Characterizations of the Generalized Jacobian

Collaborators (1)

Ling Li

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