Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
Services
Author
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I.
Basic math.
II.
Pricing and Hedging.
III.
Explicit techniques.
IV.
Data Analysis.
V.
Implementation tools.
1.
Finite differences.
2.
Gauss-Hermite Integration.
3.
Asymptotic expansions.
4.
Monte-Carlo.
5.
Convex Analysis.
A.
Basic concepts of convex analysis.
B.
Caratheodory's theorem.
C.
Relative interior.
D.
Recession cone.
E.
Intersection of nested convex sets.
F.
Preservation of closeness under linear transformation.
G.
Weierstrass Theorem.
H.
Local minima of convex function.
I.
Projection on convex set.
J.
Existence of solution of convex optimization problem.
K.
Partial minimization of convex functions.
L.
Hyperplanes and separation.
M.
Nonvertical separation.
N.
Minimal common and maximal crossing points.
O.
Minimax theory.
P.
Saddle point theory.
Q.
Polar cones.
R.
Polyhedral cones.
S.
Extreme points.
T.
Directional derivative and subdifferential.
U.
Feasible direction cone, tangent cone and normal cone.
V.
Optimality conditions.
W.
Lagrange multipliers for equality constraints.
X.
Fritz John optimality conditions.
Y.
Pseudonormality.
Z.
Lagrangian duality.
a.
Geometric multipliers.
b.
Dual problem.
c.
Connection of dual problem with minimax theory.
[.
Conjugate duality.
VI.
Basic Math II.
VII.
Implementation tools II.
VIII.
Bibliography
Notation.
Index.
Contents.
Lagrangian duality.
e consider the following problem.
Problem
(Primal problem). Find
where
,
,
and
.
We introduce the notation
a.
Geometric multipliers.
b.
Dual problem.
c.
Connection of dual problem with minimax theory.
Notation.
Index.
Contents.
Copyright 2007
