EECS260 Optimization (Fall semester 2020): project 3

Project 3 consists of participating in class discussions of several papers that apply optimisation algorithms to specific problems. You should read each paper ahead of time and be prepared to ask questions (or to answer them). The first thing to understand is the optimisation problem, i.e., what the variables are and what the objective function is (and why it makes sense to formulate the problem in that way). Example questions (not all may apply): is the problem constrained/unconstrained/smooth/line-search/trust-region...? If it uses a line search, does it satisfy the Wolfe (or other) conditions? Does the paper use a standard optimisation algorithm, or are there ad-hoc modifications, and if so what are the consequences? Are there better choices of algorithm (take into account ease of implementation, scalability wrt problem size, etc.)? Is the empirical evaluation fair and conclusive (competing methods are often presented at a disadvantage)? What is the stopping condition or convergence criterion? Does the problem have a unique optimum? How important is the optimisation part in the paper's main objectives? Be critical.

• Oct. 29. Presenter: Suryabhan Singh Hada.

  • Carreira-Perpiñán, M. Á. and Hada, S. S.: "Counterfactual explanations for oblique decision trees: exact, efficient algorithms". AAAI 2021 and arXiv:2103.01096.
  • Carreira-Perpiñán, M. Á. and Hada, S. S.: "Inverse classification with logistic and softmax classifiers: efficient optimization". arXiv:2309.08945.

• Nov. 5. Presenter: Arman Zharmagambetov.

  • SVM optimisation: CSE176 lecture notes.
  • Wang, W. and Carreira-Perpiñán, M. Á.: "The role of dimensionality reduction in classification". AAAI 2014 and arXiv:1405.6444.

• Nov. 12. Presenters: Jiaming Lin.

  • SGD for strong convex objectives: section 4.2 of Bottou, L., Curtis, F. E. and Nocedal, J.: "Optimization methods for large-scale machine learning". SIAM Review, 2018.

• Nov. 19. Presenters: Hamid Rajabi, Magzhan Gabidolla.

  • Xu, S. et al.: "Distributed estimation over sensor networks based on distributed conjugate gradient strategies". IET Signal Processing, 2016.
  • Wright, S.: "Coordinate descent algorithms". Math. Prog., 2016.

• Dec. 3. Presenters: Yerlan Idelbayev, Ignacio Romero.

  • Idelbayev, Y. and Carreira-Perpiñán, M. Á.: "Low-rank compression of neural nets: learning the rank of each layer". CVPR 2020.
  • Zhu, D. and Li, C.: "Accelerated image reconstruction in fluorescence molecular tomography using a nonuniform updating scheme with momentum and ordered subsets methods". J. Biomedical Optics, 2016.

• Dec. 10. Presenters: Anni Zhao, Pedro Andrés Sánchez Pérez.

  • Hespanhol, P. and Quirynen, R.: "A real-time iteration scheme with quasi-Newton Jacobian updates for nonlinear model predictive control". European Control Conference (ECC 2018).
  • Zohrizadeh et al.: "A survey on conic relaxations of optimal power flow problem". European J. Operational Research, 2020.
    Neumann et al.: "Approximating power flow and transmission losses in coordinated capacity expansion problems". arXiv, 2020.

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