CMPS 290c Spring '08: Convex Sets, Functions, Optimization, and Duality, Study notes of Computer Graphics

The topics covered in the cmps 290c course offered in spring '08. The course material includes convex sets with discussions on affine sets, convex combinations, and cones. Convex functions are also covered, with an emphasis on definitions of convexity and optimization problems. The document concludes with duality, which involves lagrangian functions, dual problems, and complementary slackness.

Typology: Study notes

Pre 2010

Uploaded on 08/19/2009

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CMPS 290c Topics - Spring ’08
1. Convex Sets
Affine sets
Convex sets
Convex combinations and convex hull
cones and conic combinations
hyperplanes and half-spaces
Norms and Norm balls
polyhedra
Convexity preserving operations: intersection, image of convex
set under affine transformation
Perspective function
dual cones, generalized (conic) inequalities, minimum/minimal
elements
Separating hyperplane theorem
Supporting hyperplane theorem
2. Convex functions
Definitions of convexity, concavity, strict convexity
convex if every restriction to a line is convex
First order condition
Second order condition
Epigraphs and sub-level sets, Jensen’s inequality
Closure under: non-negative weighted sums, composition with
affine functions, point-wise max and sup, composition with scalar
function, minimization over convex set
conjugate function
quasi-convexity
3. Optimization problems
Problems in standard form
Domain; feasible, optimal, and locally optimal points
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CMPS 290c Topics - Spring ’

  1. Convex Sets
    • Affine sets
    • Convex sets
    • Convex combinations and convex hull
    • cones and conic combinations
    • hyperplanes and half-spaces
    • Norms and Norm balls
    • polyhedra
    • Convexity preserving operations: intersection, image of convex set under affine transformation
    • Perspective function
    • dual cones, generalized (conic) inequalities, minimum/minimal elements
    • Separating hyperplane theorem
    • Supporting hyperplane theorem
  2. Convex functions
    • Definitions of convexity, concavity, strict convexity
    • convex if every restriction to a line is convex
    • First order condition
    • Second order condition
    • Epigraphs and sub-level sets, Jensen’s inequality
    • Closure under: non-negative weighted sums, composition with affine functions, point-wise max and sup, composition with scalar function, minimization over convex set
    • conjugate function
    • quasi-convexity
  3. Optimization problems
    • Problems in standard form
    • Domain; feasible, optimal, and locally optimal points
  • Implicit vs. Explicit constraints
  • convex optimization problems
  • optimality conditions for differentiable f 0
  • Transforming problems and equivalent problems: slack variables, adding/eliminating equality constraints, epigraph form
  • linear programming
  • quad. programming, positive semi-definite matrices, hessians, and local/global minima
  • Multicriterion optimization and pareto optimal values
  1. Duality
  • Lagrangian L(x, λ, ν), Lagrange dual function g(λ, ν)
  • Lower bound property of g(λ, ν)
  • Soft constraint interpretation of g(λ, ν)
  • Dual problem
  • Dual of Dual (for linear programming)
  • Weak and strong duality
  • Slater’s constraint qualification
  • complementary slackness
  • (KKT conditions)