Stat 202C: Monte Carlo Methods for Optimization 

 MW 2-3:15 pm, Spring 2017, Math Science 5128

  [syllabus.pdf] 

Course Description

This graduate level course introduces Monte Carlo methods for optimization, estimation and learning, including: Importance sampling; Sequential importance sampling; Markov chain Monte Carlo (MCMC) sampling techniques including Gibbs samplers, Metropolis/Hastings and various improvements; Simulated annealing; Exact sampling techniques; Convergence analysis; Data augmentation; Cluster sampling, such as Swendsen-Wang and SW-cuts; Equi-energy and multi-domain sampler; and Mapping the energy landscapes.

Prerequisites
Textbooks
   The lectures will be based on the following book draft.
Instructors
Grading Plan: 4 units, letter grades
    The grade will be based on four parts
        2 homework                                       20%
        3 small projects                                  45%
             Project 1:  Importance sampling for counting the number of SAWs in a lattice (15%) 
             Project 2:  Exact sampling of Potts model with Gibbs sampler    (15%)
             Project 3:  Cluster sampling for Potts model using Swerndsen-Wang method (15%)
        Final exam                                         35%

Tentative List of Topics

   Chapter 1,   Introduction to Monte Carlo Methods                    [Lect1.pdf]                                                                                                  
   1, Monte Carlo methods in science and enginnering 
      -- Simulation, estimation, sampling, optimization and learning.
   2, Topics and issues in Monte Carlo methods

  Chapter 2,   Sequential Monte Carlo                               [project_1.pdf]
   1. Importance sampling and weighted samples 
   2. Advanced importance sampling techniques 
   3. Framework for sequential Monte Carlo 
         (selection, pruning, resampling, ...)
   4, Application on learning log-linear/Gibbs models            
   5. Application: particle filtering in object tracking         

  Chapter 3,  Backgrounds on Markov Chains                             
   1. The transition matrix 
   2. Topology of transition matrix: communication and period 
   3. Positive recurrence and invariant measures 
   4. Ergodicity theorem                 
                                                         
   Chapter 4, Metropolis methods and its variants                           
   1. Metropolis algorithm and the Hastings's generalization
   2. Special case: Metropolized independence sampler    
   3. Reversible jumps and trans-dimensional MCMC           
 
   Chapter 5 Gibbs sampler and its variants                               
   1. Gibbs sampler                           
   2. generalizations: 
       Hit-and-run, Multi-grid, generalized Gibbs, Metropolized Gibbs
   3. Data association and data augmentation
   4. Slice sampling 

   Chapter 6  Clustering sampling                                      
   1. Ising/Potts models
   2. Swendsen-Wang and clustering sampling   
   3. Three interpretations of the SW method 

  Chapter 7 Langevin Dynamics
   1. Hamiltonian Monte Carlo
   2. Langiven dynamics used in machine learning
       Gibbs Reaction and Diffusion equations, Alternative Back-propagation

  Chapter 8 Convergence analysis                                       
   1. Monitoring and diagnosing convergence 
   2*. Contraction coefficient 
   3. Puskin's order 
   4*. Eigen-structures of the transition matrix 
         (Perron-Frobenius theorem, spectral theorem)
   5. Geometric bounds 
   6*. Exact analysis on independence Metropolised Sampler (IMS) 
   7*. First hitting time analysis and bounds for IMS (paper) 
   8. Path coupling techniques.
        Bounds for Gibbs sampler and Swendson-Wang algorithm (paper).
   * discussed in previous Chapters.
          
   Chapter 9  Exact sampling                                        
   1. Coupling from the past CFTP  
   2. Bounding chains

  Chapter 10 Advanced topics                                           
   1. Equi-energy and mult-domain sampler                 
   2. Wang-Landau algorithm
   3. Stochastic gradient
   4. Mapping the energy landscape and case studies
   5. Comparing the clustering algorithms
   6. Landscapes for curriculum learning