Notes for Lecture 20 are not available on MIT OpenCourseWare.
| LEC # | TOPICS | LECTURE NOTES |
|---|---|---|
| 1 |
Introduction Mathematical optimization; least-squares and linear programming; convex optimization; course goals and topics; nonlinear optimization. |
(PDF) |
| 2 |
Convex sets Convex sets and cones; some common and important examples; operations that preserve convexity. |
(PDF) |
| 3 |
Convex functions Convex functions; common examples; operations that preserve convexity; quasiconvex and log-convex functions. |
(PDF) |
| 4 |
Convex optimization problems Convex optimization problems; linear and quadratic programs; second-order cone and semidefinite programs; quasiconvex optimization problems; vector and multicriterion optimization. |
(PDF) |
| 5 |
Duality Lagrange dual function and problem; examples and applications. |
(PDF) |
| 6 |
Approximation and fitting Norm approximation; regularization; robust optimization. |
(PDF) |
| 7 |
Statistical estimation Maximum likelihood and MAP estimation; detector design; experiment design. |
(PDF) |
| 8 |
Geometric problems Projection; extremal volume ellipsoids; centering; classification; placement and location problems. |
(PDF) |
| 9 |
Filter design and equalization FIR filters; general and symmetric lowpass filter design; Chebyshev equalization; magnitude design via spectral factorization. |
(PDF) |
| 10 |
Miscellaneous applications Multi-period processor speed scheduling; minimum time optimal control; grasp force optimization; optimal broadcast transmitter power allocation; phased-array antenna beamforming; optimal receiver location. |
(PDF) |
| 11 |
l1 methods for convex-cardinality problems Convex-cardinality problems and examples; l1 heuristic; interpretation as relaxation. |
(PDF) |
| 12 |
l1 methods for convex-cardinality problems (cont.) Total variation reconstruction; iterated re-weighted l1; rank minimization and dual spectral norm heuristic. |
(PDF - 1.4MB) |
| 13 |
Stochastic programming Stochastic programming; “certainty equivalent” problem; violation/shortfall constraints and penalties; Monte Carlo sampling methods; validation. |
(PDF) |
| 14 |
Chance constrained optimization Chance constraints and percentile optimization; chance constraints for log-concave distributions; convex approximation of chance constraints. |
(PDF) |
| 15 |
Numerical linear algebra background Basic linear algebra operations; factor-solve methods; sparse matrix methods. |
(PDF) |
| 16 |
Unconstrained minimization Gradient and steepest descent methods; Newton method; self-concordance complexity analysis. |
(PDF) |
| 17 |
Equality constrained minimization Elimination method; Newton method; infeasible Newton method. |
(PDF) |
| 18 |
Interior-point methods Barrier method; sequential unconstrained minimization; self-concordance complexity analysis. |
(PDF) |
| 19 |
Disciplined convex programming and CVX Convex optimization solvers; modeling systems; disciplined convex programming; CVX. |
(PDF) |
| 20 | Conclusions |
