Teaching

A minicourse on Stochastic Programming - CMA (2021)

  1. Definitions and Examples,
  2. Sample Average Approximation (SAA),  
  3. Two-Stage Stochastic Linear Programming (2SLP),
  4.  Gap estimation
  5. Multistage Stochastic Programming
  6. Chance Constrained Problems
  7. Project: Power Generation Planning under Uncertainty

Problem's data: 2SLP  Deterministic Equivalent L-Shaped Method  Progessive Heding algorithm

Optimization for data science - Mines ParisTech (2020)

  1. Optimality conditions
  2. Stochastic gradient methods
  3. The Frank-Wolfe method
  4. (Projected) Subgradient method
  5. DC Programming in a nutshell

Nonlinear Programming - CMA (2020)

  1. Optimality conditions 
  2. Algorithms for unconstrained optimization
  3. Algorithms for constrained optimization
  4. Project: The Hanging Chain problem
Basic Course on Stochastic Programming - IMPA (2016) 
Below only lectures on multistage stochastic programming and Bundle methods applied to stochastic programming. 
Visite the course webpage for the full course and slides.
 

Multistage stochatic programming: Example and main definitions

 

Multistage stochatic programming: Optimality conditions

 

Multistage stochatic programming: The linear case

 

Multistage stochastic linear programming: Block separable recourse

 

Multistage stochastic programming: Dealing with nonantecipativity constraints

 

Multistage stochastic programming: Multiplier method

 

Multistage stochastic linear programming: Stochastic dual dynamic programming

 

Bundle methods for stochastic programs: Proximal bundle method

 

Bundle methods for stochastic programs: Level bundle method

 

Oracles with on-demand accuracy for two-stage programming problems

  


Cálculo Numérico - UERJ


Mestrado Profissional em Métodos Matemáticos em Finanças - IMPA
 

Otimização I


Doutorado - IMPA 
Métodos computacionais de otimização (2013)

Minicurso - UFPR 
Tomada de decisão sob incerteza. Uma abordagem do ponto de vista da otimização

Minicurso - FGV EMAp
Subpages (1): Students
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