Convex Optimization I

Incarcat la data: 19 Aprilie 2011

Autor: Admin

By Stephen Boyd - Stanford University
Licence: Creative Commons Attribution-NonCommercial-ShareAlike 3.0

Course Description:
Concentrates on recognizing and solving convex optimization problems that arise in engineering.Topics include: Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.Prerequisites: Good knowledge of linear algebra. Exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.
Lectures:
Lecture 1 - Introduction to Convex Optimization I

Introduction, Examples, Solving Optimization Problems, Least-Squares, Linear Programming, Convex Optimizations, How To Solve?, Course Goals

 
Lecture 2 - Guest Lecturer: Jacob Mattingley

Guest Lecturer: Jacob Mattingley, Logistics, Agenda, Convex Set, Convex Cone, Polyhedra, Positive Semidefinite Cone, Operations That Preserve Convexity, Intersection, Affine Function, Generalized Inequalities, Minimum And Minimal Elements, Supporting Hyperlane Theorem, Minimum And Minimal Elements Via Dual Inequalities

 
Lecture 3 - Logistics

Logistics, Convex Functions, Examples, Restriction Of A Convex Function To A Line, First-Order Condition, Examples (FOC And SOC), Epigraph And Sublevel Set, Jensen's Inequality, Operations That Preserve Convexity, Pointwise Maximum, Pointwise Maximum, Composition With Scalar Functions, Vector Composition

 
Lecture 4 - Vector Composition

Vector Composition, Perspective, The Conjugate Function, Quasiconvex Functions, Examples, Properties (Of Quasiconvex Functions), Log-Concave And Log-Convex Functions, Properties (Of Log-Concave And Log-Convex Functions), Examples (Of Log-Concave And Log-Convex Functions)

 
Lecture 5 - Optimal And Locally Optimal Points

Optimal And Locally Optimal Points, Feasibility Problem, Convex Optimization Problem, Local And Global Optima, Optimality Criterion For Differentiable F0, Equivalent Convex Problems, Quasiconvex Optimization, Problem Families, Linear Program

 
Lecture 6 - (Generalized) Linear-Fractional Program

(Generalized) Linear-Fractional Program, Quadratic Program (QP), Quadratically Constrained Quadratic Program (QCQP), Second-Order Cone Programming, Robust Linear Programming, Geometric Programming, Example (Design Of Cantilever Beam), GP Examples (Minimizing Spectral Radius Of Nonnegative Matrix)

 
Lecture 7 - Generalized Inequality Constraints

Generalized Inequality Constraints, Semidefinite Program (SDP), LP And SOCP As SDP, Eigenvalue Minimization, Matrix Norm Minimization, Vector Optimization, Optimal And Pareto Optimal Points, Multicriterion Optimization, Risk Return Trade-Off In Portfolio Optimization, Scalarization, Scalarization For Multicriterion Problems

 
Lecture 8 - Lagrangian

Lagrangian, Lagrange Dual Function, Least-Norm Solution Of Linear Equations, Standard Form LP, Two-Way Partitioning, Dual Problem, Weak And Strong Duality, Slater's Constraint Qualification, Inequality Form LP, Quadratic Program, Complementary Slackness

 
Lecture 9 - Complementary Slackness

Complementary Slackness, Karush-Kuhn-Tucker (KKT) Conditions, KKT Conditions For Convex Problem, Perturbation And Sensitivity Analysis, Global Sensitivity Result, Local Sensitivity, Duality And Problem Reformulations, Introducing New Variables And Equality Constraints, Implicit Constraints, Semidefinite Program

 
Lecture 10 - Applications Section of Course

Applications Section Of The Course, Norm Approximation, Penalty Function Approximation, Least-Norm Problems, Regularized Approximation, Scalarized Problem, Signal Reconstruction, Robust Approximation, Stochastic Robust LS, Worst-Case Robust LS

 
Lecture 11 - Statistical Estimation

Statistical Estimation, Maximum Likelihood Estimation, Examples, Logistic Regression, (Binary) Hypothesis Testing, Scalarization, Experiment Design, D-Optimal Design

 
Lecture 12 - Continue On Experiment Design

Continue On Experiment Design, Geometric Problems, Minimum Volume Ellipsoid Around A Set, Maximum Volume Inscribed Ellipsoid, Efficiency Of Ellipsoidal Approximations, Centering, Analytic Center Of A Set Of Inequalities, Linear Discrimination

 
Lecture 13 - Linear Discrimination (Cont.)

Linear Discrimination (Cont.), Robust Linear Discrimination, Approximate Linear Separation Of Non-Separable Sets, Support Vector Classifier, Nonlinear Discrimination, Placement And Facility Location, Numerical Linear Algebra Background, Matrix Structure And Algorithm Complexity, Linear Equations That Are Easy To Solve, The Factor-Solve Method For Solving Ax = B, LU Factorization

 
Lecture 14 - LU Factorization (Cont.)

LU Factorization (Cont.), Sparse LU Factorization, Cholesky Factorization, Sparse Cholesky Factorization, LDLT Factorization, Equations With Structured Sub-Blocks, Dominant Terms In Flop Count, Structured Matrix Plus Low Rank Term

 
Lecture 15 - Algorithm Section Of The Course

Algorithm Section Of The Course, Unconstrained Minimization, Initial Point And Sublevel Set, Strong Convexity And Implications, Descent Methods, Gradient Descent Method, Steepest Descent Method, Newton Step, Newton's Method, Classical Convergence Analysis, Examples

 
Lecture 16 - Continue On Unconstrained Minimization

Continue On Unconstrained Minimization, Self-Concordance, Convergence Analysis For Self-Concordant Functions, Implementation, Example Of Dense Newton System With Structure, Equality Constrained Minimization, Eliminating Equality Constraints, Newton Step, Newton's Method With Equality Constraints

 
Lecture 17 - Newton's Method (Cont.)

Newton's Method (Cont.), Newton Step At Infeasible Points, Solving KKT Systems, Equality Constrained Analytic Centering, Complexity Per Iteration Of Three Methods Is Identical, Network Flow Optimization, Analytic Center Of Linear Matrix Inequality, Interior-Point Methods, Logarithmic Barrier

 
Lecture 18 - Logarithmic Barrier

Logarithmic Barrier, Central Path, Dual Points On Central Path, Interpretation Via KKT Conditions, Force Field Interpretation, Barrier Method, Convergence Analysis, Examples, Feasibility And Phase I Methods

 
Lecture 19 - Interior-Point Methods (Cont.)

Interior-Point Methods (Cont.), Example, Barrier Method (Review), Complexity Analysis Via Self-Concordance, Total Number Of Newton Iterations, Generalized Inequalities, Logarithmic Barrier And Central Path, Barrier Method, Course Conclusion, Further Topics

 

Source: http://academicearth.org/courses/convex-optimization-i

Textul de mai sus reprezinta un extras din "Convex Optimization I". Pentru versiunea completa a documentului apasa butonul Download si descarca fisierul pe calculatorul tau. Prin descarcarea prezentei lucrari stiintifice, orice utilizator al site-ului www.studentie.ro declara si garanteaza ca este de acord cu utilizarile permise ale acesteia, in conformitate cu prevederile legale ablicabile in domeniul proprietatii intelectuale si in domeniul educatiei din legislatia in vigoare.

In cazul in care intampini probleme la descarcarea fisierului sau documentul nu este nici pe departe ceea ce se doreste a fi te rugam sa ne anunti. Raporteaza o eroare

Important!

Referatele si lucrarile oferite de Studentie.ro au scop educativ si orientativ pentru cercetare academica.

Iti recomandam ca referatele pe care le downloadezi de pe site sa le utilizezi doar ca sursa de inspiratie sau ca resurse educationale pentru conceperea unui referat nou, propriu si original.

Sandale casual dama ECCO Touch Plateau (Negre) Sandale casual dama ECCO Touch Plateau (Negre) Sandalele ECCO Touch Plateau sunt confectionate din piele moale cu detalii metalice(tinte). Sunt...