Optimization Course

Approximate split of the video lectures by week

1.  Introduction and Linear Algebra refresh (watch at home before Class 1)

• Lecture 1a, Introduction; Examples of unconstrained and constrained optimization problems:

• Lecture 1b, Linear Algebra refresh

2. Gradient and Hessian of Multivariate Function

• Lecture 2-3: Gradient and Hessian of a Multivariate Function 

• Differential and gradient of a multivariate function, 2022

• Homework on analytical and numerical computation of gradient and Hessian
  
  

3. Convexity and optimality 

• Lecture 4-5: Convex sets and functions (enhanced)

• Lecture 6 (enhanced).Local and global minimum. Sufficient and necessary optimality conditions
  
  

4.  1d minimization

• Bisection method for finding root and minimum of 1D function 

• Golden section method of 1D minimization

• Quadratic interpolation method of 1D minimization

• Cubic interpolation method of 1D minimization  

•  Multidimensional optimization with line search
  
  
  

5. Gradient, Newton and Gauss-Newton methods

• Gradient descent method (steepest descent)   

• Newton method for multidimensional minimization. Part 1 Part 2 

•  The notion of Jacobian  (the first 3 min of the video:  An easy way to compute Jacobian and gradient with forward and back propagation in a graph)

•  Newton and Gauss-Newton methods for nonlinear system of equations and least-squares problem
  
  
  

6. Conjugate gradient method  

• Conjugate gradients 1: Introduction

• Conjugate gradients 2: Inner product; Gram-Schmidt orthogonalization

• Conjugate gradients 3: Conjugate directions; Expanding manifold

• Conjugate gradients 4: Derivation of CG method

• Preconditioning: Gradient Descent, Conjugate Gradients and SESOP   (watch till SESOP, 7:30 min)
  
  

7. Truncated Newton and Quasi-Newton 

• Lecture12, Section on Truncated Newton method (from time 44:04 till end) 

• Quasi-Newton Optimization Methods: BFGS, L-BFGS

8. Neural networks, CNN, Jacobian, back-propagation in matrix form

• Intro to Neural Networks  

• Convolutional Neural Networks in 10 minutes 

• Easy way to compute Jacobian and gradient with forward and back propagation in graph

• Gradient of Neural Network in matrix form: Part 1, Part 2  

9. Optimization with constraints 

•   Lecture 13, Optimization with constraints,

•   Lecture 14, Lagrange multipliers and penalty function method, till 32:27

10. Augmented Lagrangian

•  Penalty function and Augmented Lagrangian methods 2013

•   Lecture 14 Lagrange multipliers and penalty function method. Augmented Lagrangian,  starting from the time 43:00 till the end

• ADMM - Alternating Direction Method of Multipliers
  
  

11.  Lecture 15 Minimax theorem, game theory and Lagrange duality

12.  Lecture 16 Conic programming 1  (in particular, semidefinite programming)

13.    Lecture 16b-17 Conic programming 2
        - Penalty method for semidefinite programming and homework on linear matrix approximation
      - Lagrange Duality: Conic Programming vs Nonlinear Programming

14 (tentative).   Denoising, deconvolution, and computed tomography using total variation penalty

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GUI 

Useful Links                                      

                                      Yalmip 3

                                      Arkadi Nemirovski,  Lectures on modern optimization  

                                     Freemat   -  a free Matlab-like coding environment

  Numerical Recipes - Books Online

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Contents of the video lectures

Lecture 1a, Introduction; Examples of unconstrained  and constrained optimization problems:

       Linear regression  

       Function approximation with feed-forward neural network   

       Resource assignment with Linear Programming

Lecture 1b, Linear algebra refresh:

Linear and affine subspace; 

Lp norm of a vector

Matrix norms

Inner product of matrices

Eigenvalue decomposition

Matrix polynomials and  functions

Positive (semi) definite matrices

Lecture 2-3: Derivatives of multivariate functions: Gradient and Hessian

  Total differential and gradient

  Level sets and directional derivatives

  Hessian matrix

  Second directional derivative

  Example: Gradient and Hessian of linear and quadratic function

  Taylor expansion of multivariate functions

  Gradient of a function of a matrix

  Example: Gradient of  a neural network

Lecture 4-5: Convex sets and functions

   Definition of convex set and function.

   Properties of convex sets

   Properties of convex functions

   Extended value functions

   Epigraph

   Convex combination and convex hull

   Jensen inequality

   Gradient inequality. Hessian of a convex function is positive (semi) definite

Lecture 6. Local and global minimum.  Sufficient and necessary unconstrained optimality conditions

Lecture 7 (in Hebrew).  Optimality conditions; iterative methods of one-dimensional optimization  (line search).

   Optimality conditions

   One-dimensional optimization, Bisection method

    Golden section method

    Quadratic interpolation method

    Cubic interpolation method

     Note: First part of  Lecture 7 (optimality conditions) repeats in Hebrew  the second part of Lecture 6.

Lecture 8  Iterative methods of multivariate unconstrained optimization

   General line search method

   Choice of step size: Exact optimization, Backtracking,  Armijo stopping rule

   Steepest descent (gradient descent)

    Newton method

Lecture 9 More on Newton method

   Newton method for nonlinear equations

   Modified Newton method: enforcing descent direction 

   Solving symmetric system of equations via Cholesky factorization

   Least-squares problem: Gauss-Newton and  Levenberg–Marquardt methods

Lecture 10 Method of Conjugate Gradients 1

   Motivation

   Scalar product, definition  and examples

   Gram-Schmidt orthogonalization

   Q-conjugate (Q-orthogonal) directions

   Minimization of a quadratic function using conjugate directions

Lecture 11 Method of Conjugate Gradients 2

Derivation of the method of Conjugate Gradients

Summary of CG method

Convergence rate of CG

Preconditioning 

Truncated Newton method

Computational burden to compute function, gradient and Hessian-vector product is about the same

Lecture 12 Sequential subspace optimization (SESOP) method and Quasi-Newton BFGS

SESOP method

Fast optimization over subspace

Quasi-Newton methods

How to approximate Hessian

Approximation of inverse Hessian, Sherman-Morrison formula

Broyden family Quasi-Newton methods, DFP, BFGS

Initialization and convergence properties 

Lecture 13. Summary of unconstrained optimization. Optimization with constraints

Summary of unconstrained optimization

Constrained optimization, optimality conditions

Karush - Kuhn - Tucker (KKT) first order optimality conditions

Penalty function method

Lecture 14 Lagrange multipliers and penalty function method. Augmented Lagrangian

Lagrange multipliers via penalty function method

Penalty for equality constraints

Barrier method

Augmented Lagrangian method (or method of multipliers)

Augmented Lagrangian for equality constraints

Optimal solution in one step, when multipliers are known

Lecture 15 Minimax theorem, game theory and Lagrange duality

Introduction

Minimax theorem

Game interpretation of Minimax

Saddle point theorem

Minimax of Lagrangian; 

Dual problem and  weak duality 

Strong duality

Slayter condition for strong duality

Examples of dual problems:

         Quadratic program

          Linear program

Lecture 16 Conic programming 1

Introduction

Examples of cones - R^n+, Lorenz cone, cone of positive semidefinite matrices

Semidefinite programming (SDP)

Dual cone , Dual conic problem, weak duality 

Strong conic duality and complementarity slackness 

Example: Dual SDP problem

Minimax problem

Chebyshev approximation

Lecture 16b-17 Conic programming 2

Continue with Chebyshev approximation, which starts at

Complex Chebyshev approximation

Conversion of different problems to SDP

Lemma of Schur complement 

Minimize maximal eigenvalue of symmetric matrix

Linear matrix approximation

Expression of Linear program via SDP

Conic quadratic program via SDP

Barrier method for solution of conic programming problem

Examples of barriers

Matrix functions

Gradient of trace of matrix function

Gradient of the barrier log det A (x)

Homework on analytical and numerical computation of gradient and Hessian

Penalty method for semidefinite programming and homework on linear matrix approximation

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Contents of the video lectures (time pointers included)

Lecture 1a, Introduction; Examples of unconstrained  and constrained optimization problems:

       Linear regression   (slides 10:08, 11:56)

       Function approximation with feed-forward neural network    12:15  (slides 16:15, 24:44, 27.21)

       Resource assignment with Linear programming  27:40 (slides 33:02, 35:42)

Lecture 1b, Linear algebra refresh:

Linear and affine subspace;  0:0 (slides 05:39, 07:07

Lp norm of a vector 7:24 (slides 9:44 13:45

Matrix norms - 14:52 (slides 18:29)

Inner product of matrices - 19:30 (slides  22:40)

Eigenvalue decomposition -23:15 (slides 29:20, 31:47)

Matrix  polynomials and  functions  31:45 (35:12, 40:25)

Positive (semi) definite matrices - 41:17 (slides 45:41)

Lecture 2-3: Derivatives of multivariate functions: Gradient and Hessian

  Total differential and gradient -  0:0 (slides 5:14, 13:16)

  Level sets and directional derivatives - 13:17 (slides 23:07)

  Hessian matrix - 23:20 (slides 28:50, 33:54)

  Second directional derivative - 34:45  (slides 35:55)

  Example: Gradient and Hessian of linear and quadratic function -39:04 (slides 39:04)

  Taylor expansion of multivariate functions 46:30 (slides 52:56)

  Gradient of a function of a matrix - 53:23 (slides 58:54)

  Example: Gradient of  a neural network  58:56 (slides 1:09:43)

  Homework - 1:09:45

Lecture 4-5: Convex sets and functions

   Definition of convex set and function. Properties of convex sets - 0:0 (slides 03:05, 9:15, 9:53, 19:10)

   Properties of convex functions - 19:10 (slides 20:52, 26:15)

   Extended value functions - 26:41     (slides 31:22)

   Epigraph - 31:31 (slides 34:34)

   Convex combination and convex hull 34:38 (slides 37:18)

   Jensen inequality 39:02 (slides 43:24)

   Gradient inequality. Hessian of a convex function is positive (semi) definite 43:28  (slides 47:47)

Lecture 6. Local and global minimum.  Sufficient and necessary unconstrained optimality conditions

(slides 2:10, 7:54, 12:24, 17:34, 21:55)

Lecture 7 (in Hebrew).  Optimality conditions; iterative methods of one-dimensional optimization  (line search).

   Optimality conditions - 0:0

   One-dimensional optimization, Bisection method 13:13 (slides 23:13)

    Golden section method 23:17 (slides 32:02)

    Quadratic interpolation method 32:04 (slides 36:39, 43:22, 47:00)

    Cubic interpolation method 47:04 (slides 51:07, 57:35)

     Note: First part of  Lecture 7 (optimality conditions) repeats in Hebrew  the second part of Lecture 6.

Lecture 8  Iterative methods of multivariate unconstrained optimization

   General line search method 0:0 (slides 05:40)

   Choice of step size: Exact optimization, Backtracking,  Armijo stopping rule 5:53 (slides 17:05, 20:44)

   Steepest descent (gradient descent) 20:55 (slides 36:07)

    Newton method 36:22 (slides 45:18, 56:04)

    End - 56:15 (after this time - garbage from  previous lecture)

Lecture 9 More on Newton method

   Newton method for nonlinear equations 0:0 (slides 5:28, 8:24)

   Modified Newton method: enforcing descent direction  8:26 (slides 15:54)

   Solving symmetric system of equations via Cholesky factorization 15:58 (slides 22:35, 28:39)

   Least-squares problem: Gauss-Newton and  Levenberg–Marquardt methods 28:44 (slides 33:39, 43:02, 52:27, 54:18)

Lecture 10 Method of Conjugate Gradients 1

   Motivation 0:0

   Scalar product, definition 4:47 (slide on 8:53), and examples 8:56 (slides 13:00)

   Gram-Schmidt orthogonalization 13:07 (slides 21:13)

   Q-conjugate (Q-orthogonal) directions 21:18 (slides 26:24)

   Minimization of a quadratic function using conjugate directions 26:30 (slides 36:14 or 38:13)

   Expanding manifold property 38:10 (slides 48:53 and 50:58)

Lecture 11 Method of Conjugate Gradients 2

Derivation of the method of Conjugate Gradients 0:0 (slides 5:34, 12:11, 19:32)

Summary of CG method 19:33 (slides 27:06)

Convergence rate of CG 27:08 (slides 32:57)

Preconditioning  32:58 (slides 44:03)

Truncated Newton method 44:04 (slide 50:20)

Computational burden to compute function, gradient and Hessian-vector product is about the same 50:20 (slides 52:36, 57:14)

Lecture 12 Sequential subspace optimization (SESOP) method and Quasi-Newton BFGS

SESOP method 0:42  (slides 7:35)

Fast optimization over subspace  7:37 (slides 15:36)

Quasi-Newton methods 15:37  (slides 22:42)

How to approximate Hessian 22:43 (slide 32:24 )

Approximation of inverse Hessian, Sherman-Morrison formula 32:25 (slide 37:43)

Broyden family Quasi-Newton methods, DFP, BFGS  37:44  (slides  41:06, 44:10, 47:01 )

Initialization and convergence properties  47:02  (slide 53:26 )

Lecture 13. Summary of unconstrained optimization. Optimization with constraints

Summary of unconstrained optimization 0:0 (slide 4:59, one-dimensional methods)

Multivariate methods 05:03 (slides 13:47, 15:41)

Constrained optimization, optimality conditions 15:44 (slides 19:02, 26:32; 30:02)

Karush - Kuhn - Tucker (KKT) first order optimality conditions 30:06 (slide 35:03 )

Penalty function method 35:05 (slides 48:36, 54:44, 57:29)

Lecture 14 Lagrange multipliers and penalty function method. Augmented Lagrangian

Lagrange multipliers via penalty function method 0:0 (slides 5:54, 16:30 )

Penalty for equality constraints 16:36 (slides penalty 21:08; optimality conditions 24:39)

Barrier method 24:44 (slide 32:24)

Augmented Lagrangian method (or method of multipliers) 32:28 (slides 41:55, 48:40, 51:08)

Augmented Lagrangian for equality constraints 51:11 (slides 53:44)

Optimal solution in one step, when multipliers are known 53:55 (slides 01:02:13 )

Lecture 15 Minimax theorem, game theory and Lagrange duality

Introduction 0:0

Minimax theorem 1:19  (slides 6:16)

Game interpretation of minimax 6:20 (slides 12:07)

Saddle point theorem 12:12 (slides 19:03)

Minimax of Lagrangian;  weak duality  19:08 (slides 29:07)

Dual problem and  weak duality  29:19 (slides 30:39)

Strong duality 30:43 (slides 39:14, 43:03)

Slayter condition for strong duality 43:10 (slides 44:33)

Examples of dual problems:

         Quadratic program 44:38 (slides 49:26)

          Linear program 49:40 (slides 57:48)

Garbage 58:36 -- till end (01:02:24)

Lecture 16 Conic programming 1

Introduction 0:0 (slides 13:43)

Examples of cones - R^n+, Lorenz cone, cone of positive semidefinite matrices13:50 (slides 24:45)

Semidefinite programming (SDP) 24:49 (slides 28:40)

Dual cone 28:43 (slides 33:46, 39:33)

Dual conic problem, weak duality  39:37  (slides 47:26)

Strong conic duality and complementarity slackness  47:34  (slides 56:52)

Example: Dual SDP problem 57:02 (slides 1:05:45)

Minimax problem 1:05:52  (slides 1:13:17, 1:16:12)

Chebyshev approximation 1:16:14 (slides  in the next video, 2:10)

Lecture 16b-17 Conic programming 2

Continue with Chebyshev approximation, which starts at 1:16:14 of the previous video (slides  2:10)

Complex Chebyshev approximation 02:16 (slides 15:25, 18:40)

Conversion of different problems to SDP 18:51

Lemma of Schur complement  19:21 (slides 25:14)

Minimize maximal eigenvalue of symmetric matrix 25:18 (slides 31:33)

Linear matrix approximation 31:36 (slides 43:40)

Expression of Linear program via SDP 43:43 (slides 45:39)

Conic quadratic program via SDP 45:40 (slides 49:57)

Barrier method for solution of conic programming problem 50:06 (slides 57:26)

Examples of barriers 57:29  (slides 1:03:12)

Matrix functions 1:03:16 (slides 1:14:53, 1:17:05)

Gradient of trace of matrix function 1:17:09 (slides 1:25:24)

Gradient of log det A  1:25:28 (slides 1:28:28)

Gradient of log det barrier 1:28:31 (slides 1:31:30)

Homework on analytical and numerical computation of gradient and Hessian ( file opt_hw_Grad_Hess_compr.avi )

Slides 5:42 6:39 10:12 15:55

Penalty method for semidefinite programming and homework on linear matrix approximation

( file SDP_penalty_method_compress.avi)

slides 3:20 3:26 10:46 11:09 18:39

mzib@cs.technion.ac.il