- Aug 27 Introduction. Chapter 1.1 - Functions of one variable - first derivative and extremes
- Aug 29 second derivative as an indicator for min/max, Chapter 1.2 - bunch on definitions
- Aug 31 analogs for function of more variables, quadratic form, postitive semidefinite matrix, Chapter 1.2 - other part
- Sep 3 Labor day (no class)
- Sep 5 semidefinite matrices, determinant criterium, Chapter 1.3
**HW 1 is due** - Sep 7 coercive functions, eigen values as criterum for semidefinite matrix , Chapter 1.3, 1.4
- Sep 10 Convex sets, Chapter 2.1
- Sep 12 Convex functions, Chapter 2.3
**HW 2 is due** - Sep 14 A-G inequality, proof and examplem, Chapter 2.4
- Sep 17 A-G inequality, definition od geometric programming, Chapter 2.4
- Sep 19 Geometric programming, Chapter 2.5
**HW 3 is due** - Sep 21 Least squares - theory, Chapter 4.1
- Sep 24 Least squares for a line, Chapter 4.1
- Sep 26 QR-factorization, Chapter 4.1
**HW 4 is due** - Midterm time Sep 27 6pm - 170 Everitt Lab - Chapters 1 and 2
- Sep 28 Projection and underdetermined system of equations Chapter 4.2 and Chapter 4.3
- Oct 1 Midterm review, generalized norms Chapter 4.4
- Oct 3 Separation and support theorems, Chapter 5.1
- Oct 5 Separation and support theorems, Chapter 5.1
- Oct 8 definitions of convex program, Chapter 5.2. Linar programming examples
- Oct 10 Linar programming examples
**HW 5 is due** - Oct 12 Definition of MP(z), Chapter 5.2
- Oct 15 examples for MP(z) sensitivity vector, definition of Lagrangian, Chapter 5.2
- Oct 17 Karush-Kuhn-Tucker theorem, Chapter 5.2
**HW 6 is due** - Oct 19 Usage of KKT, extended AG inequality, Chapter 5.2 and 5.3
- Oct 22 Constrained geometric programming, Chapter 5.3
- Oct 24 Duality on constrained geometric programming, Chapter 5.3
**HW 7 is due** - Oct 26 Dual convex programs and demonstration on linear programming, Chapter 5.4
- Oct 29 Duality gap for convex programs, Chapter 5.4
- Oct 31 Penalty method, Chapter 6.1
**HW 8 is due** - Midterm time Nov 1 6pm - 143 Altgeld Hall
- Nov 2 Penatly method, Chapter 6.2
- Nov 5 Penatly method, Chapter 6.3
- Nov 7 Newton's method for unconstrained optimization, Chapter 3.1
- Nov 9 Method of steepest descent, Chapter 3.2
- Nov 12 Designing better method, Chapter 3.3
- Nov 14 Trust region method and Broyden's method, Chapter 5.5 and Chapter 3.4
**HW 9 is due** - Nov 16
**NO CLASS** - Nov 19 Thanksgiving (no class)
- Nov 21 Thanksgiving (no class)
- Nov 23 Thanksgiving (no class)
- Nov 26 Broyden's method and preparation for BFGS, Chapter 3.4 and Chapter 3.5
- Nov 28 BFGS and DFP, Chapter 3.5
- Nov 30
**NO CLASS** - Dec 3 Semidefinite programming - introduction, same as linear programming
- Dec 5 Semidefinite programming - quadratic contraints
**HW 10 is due** - Midterm time Dec 6 6pm - 143 Altgeld Hall
- Dec 7 Semidefinite programming - MaxCut algorithm using semidefinite programming
- Dec 10 Interior point method - theoretical concept and main idea
- Dec 12
**NO CLASS** - Dec 17 8:00-11:00 AM
**FINAL EXAM**- 443 Altgeld Hall