Programación no lineal
6.252 NONLINEAR PROGRAMMING LECTURE 1: INTRODUCTION LECTURE OUTLINE • Nonlinear Programming • Application Contexts • Characterization Issue • Computation Issue • Duality • Organization
NONLINEAR PROGRAMMING min f (x),
x∈X
where • f : n → is a continuous (and usually differentiable)function of n variables • X = n or X is a subset of ous” character. • If X =
n, n
with a “continu-
the problem is called unconstrained
• If f is linear and X is polyhedral, the problem is a linear programming problem. Otherwise it is a nonlinear programming problem • Linear and nonlinear programming have traditionally been treated separately. Their methodologies have gradually come closer.TWO MAIN ISSUES • Characterization of minima − Necessary conditions − Sufficient conditions − Lagrange multiplier theory − Sensitivity − Duality • Computation by iterative algorithms − Iterative descent − Approximation methods − Dual and primal-dual methods
APPLICATIONS OF NONLINEAR PROGRAMMING • Data networks – Routing • Production planning • Resource allocation • Computer-aided design •Solution of equilibrium models • Data analysis and least squares formulations • Modeling human or organizational behavior
CHARACTERIZATION PROBLEM • Unconstrained problems − Zero 1st order variation along all directions • Constrained problems − Nonnegative 1st order variation along all feasible directions • Equality constraints − Zero 1st order variation along all directions on the constraintsurface − Lagrange multiplier theory • Sensitivity
COMPUTATION PROBLEM • Iterative descent • Approximation • Role of convergence analysis • Role of rate of convergence analysis • Using an existing package to solve a nonlinear programming problem
POST-OPTIMAL ANALYSIS • Sensitivity • Role of Lagrange multipliers as prices
DUALITY • Min-common point problem / max-intercept problemduality
Min Common Point S S Min Common Point
0 Max Intercept Point Max Intercept Point
0
(a)
(b)
Illustration of the optimal values of the min common point and max intercept point problems. In (a), the two optimal values are not equal. In (b), the set S, when “extended upwards” along the nth axis, yields the set ¯ ¯ S = {¯ | for some x ∈ S, xn ≥ xn , xi = xi , i = 1, . . . , n − 1} x ¯which is convex. As a result, the two optimal values are equal. This fact, when suitably formalized, is the basis for some of the most important duality results.
6.252 NONLINEAR PROGRAMMING LECTURE 2 UNCONSTRAINED OPTIMIZATION OPTIMALITY CONDITIONS
LECTURE OUTLINE • Unconstrained Optimization • Local Minima • Necessary Conditions for Local Minima • Sufficient Conditions for Local Minima •The Role of Convexity
MATHEMATICAL BACKGROUND • Vectors and matrices in
n
• Transpose, inner product, norm • Eigenvalues of symmetric matrices • Positive definite and semidefinite matrices • Convergent sequences and subsequences • Open, closed, and compact sets • Continuity of functions • 1st and 2nd order differentiability of functions • Taylor series expansions • Mean value theoremsLOCAL AND GLOBAL MINIMA
f(x)
x Strict Local Minimum Local Minima Strict Global Minimum
Unconstrained local and global minima in one dimension.
NECESSARY CONDITIONS FOR A LOCAL MIN • 1st order condition: Zero slope at a local minimum x∗ ∇f (x∗ ) = 0 • 2nd order condition: Nonnegative curvature at a local minimum x∗ ∇2 f (x∗ ) : Positive Semidefinite • There may exist points that satisfy...
Regístrate para leer el documento completo.