Computation and Data

Along with applications in Energy and Health, our group develops algorithms to speed up computation of optimization and machine learning algorithms.

The primary algorithms are for bi-level optimization problems, equilibrium in binary variables, and optimization problems under uncertainty. We also extend these to improve computation in machine learning and multi objective optimization.

Optimization with Equilibrium Constraints

Siddiqui, S., Gabriel, S.A., ( 2013 )

An SOS1-Based Approach for Solving MPECs with a Natural Gas Market Application

Networks and Spatial Economics, 13(2), 205-227

Robust Optimization

Siddiqui,S., Azarm, S., Gabriel, S.A., ( 2011 )

A modified Benders decomposition method for efficient robust optimization under interval uncertainty

Structural and Multidisciplinary Optimization, 44(2): 259-275

Siddiqui, S., Gabriel, S.A.,Azarm, S., ( 2015 )

Solving mixed-integer robust optimization problems with interval uncertainty using Benders decomposition

Journal of the Operational Research Society, 66: 664-673

Multiobjective Optimization

Siddiqui, S., Azarm, S., Gabriel, S.A., ( 2012 )

On improving normal boundary intersection method for generation of Pareto frontier

Structural and Multidisciplinary Optimization, 46(6): 839-852

Integer Optimization and Equilibrium

Huppmann, D., Siddiqui, S., ( 2015 )

An exact solution method for binary equilibrium problems with compensation and the power market uplift problem

In Review

Oke, O., Siddiqui, S., ( 2015 )

Efficient automated schematic map drawing using multiobjective mixed-integer programming

Computers & Operations Research, 61: 1-17

Gabriel, S.A., Siddiqui, S., Conejo, A.J., Ruiz, C., ( 2013 )

Solving Discretely-Constrained Nash-Cournot Games with an Applications to Power Markets

Networks and Spatial Economics, 13(3), 307-326

Gabriel, S.A., Conejo, A.J., Ruiz, C., Siddiqui, S., ( 2013 )

Solving discretely-constrained, mixed linear complementarity problems with applications in energy

Computers & Operations Research, 40(5): 1339-1350

Data Mining and Machine Learning

Jiang, W., & Siddiqui, S., ( 2019 )

Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent.

EURO Journal on Computational Optimization, Accepted.

Tracking global bicycle ownership patterns 

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