4.3 DIFFERENTIAL PROPERTIES OF CONVEX FUNCTIONS4.4 EXTREMA OF CONVEX FUNCTIONS; 4.5 OPTIMALITY CONDITIONS FOR CONVEX PROGRAMS; 5 - DUALITY IN NONLINEAR CONVEX PROGRAMMING; 5.1 CONJUGATE FUNCTIONS; 5.2 DUAL CONVEX PROGRAMS; 5.3 OPTIMALITY CONDITIONS AND LAGRANGE MULTIPLIERS; 5.4 DUALITY AND OPTIMALITY FOR STANDARD CONVEX PROGRAMS; 6 - GENERALIZED CONVEXITY; 6.1 QUASICONVEX AND PSEUDOCONVEX FUNCTIONS; 6.2 ARCWISE-CONNECTED SETS AND CONVEX-TRANSFORMABLE FUNCTIONS; 6.3 LOCAL AND GLOBAL MINIMA; 7 - ANALYSIS OF SELECTED NONLINEAR PROGRAMMING PROBLEMS; 7.1 QUADRATIC PROGRAMMING.

7.2 STOCHASTIC LINEAR PROGRAMMING WITH SEPARABLE RECOURSE FUNCTIONS7.3 GEOMETRIC PROGRAMMING; PART II - METHODS; 8 - ONE-DIMENSIONAL OPTIMIZATION; 8.1 NEWTON'S METHOD; 8.2 POLYNOMIAL APPROXIMATION METHODS; 8.3 DIRECT METHODS-FIBONACCI AND GOLDEN SECTION TECHNIQUES; 8.4 OPTIMAL AND GOLDEN BLOCK SEARCH METHODS; 9 - MULTIDIMENSIONAL UNCONSTRAINED OPTIMIZATION WITHOUT DERIVATIVES: EMPIRICAL AND CONJUGATE DIRECTION METHODS; 9.1 THE SIMPLEX METHOD; 9.2 PATTERN SEARCH; 9.3 THE ROTATING DIRECTIONS METHOD; 9.4 CONJUGATE DIRECTIONS; 9.5 POWELL'S METHOD; 9.6 AVOIDING LINEARLY DEPENDENT SEARCH DIRECTIONS.

9.7 FURTHER CONJUGATE DIRECTION-TYPE ALGORITHMS10 - SECOND DERIVATIVE, STEEPEST DESCENT, AND CONJUGATE GRADIENT METHODS; 10.1 NEWTON-TYPE AND STEEPEST DESCENT METHODS; 10.2 CONJUGATE GRADIENT METHODS; 10.3 CONVERGENCE OF CONJUGATE GRADIENT