Optimization : Algorithms and Consistent Approximations Contents: Unconstrained Optimization Optimality Conditions Algorithm Models and Convergence Conditions I Gradient Methods Newtons Method Methods of Conjugate Directions Quasi Newton Methods One Dimensional Optimization Newtons Method for Equations and Inequalities Finite Minimax and Constrained Optimization Optimality Conditions for Minimax Optimality Conditions for Constrained Optimization Algorithm Models and Convergence Conditions II First Order Minimax Algorithms Newtons Method for Minimax Problems Phase I. Phase II Methods of Centers Penalty Function Algorithms An Augmented Lagrangian Method. This book deals with optimality conditions algorithms and discretization tech­ niques for nonlinear programming semi infinite optimization and optimal con­ trol problems. The unifying thread in the presentation consists of an abstract theory within which optimality conditions are expressed in the form of zeros of optimality junctions algorithms are characterized by point to set iteration maps and all the numerical approximations required in the solution of semi infinite optimization and optimal control problems are treated within the context of con­ sistent approximations and algorithm implementation techniques. Traditionally necessary optimality conditions for optimization problems are presented in Lagrange F. John or Karush Kuhn Tucker multiplier forms with gradients used for smooth problems and subgradients for nonsmooth prob­ lems. We present these classical optimality conditions and show that they are satisfied at a point if and only if this point is a zero of an upper semicontinuous optimality junction. The use of optimality functions has several advantages. First optimality functions can be used in an abstract study of optimization algo­ rithms. Second many optimization algorithms can be shown to use search directions that are obtained in evaluating optimality functions thus establishing a clear relationship between optimality conditions and algorithms. Third estab­ lishing optimality conditions for highly complex problems such as optimal con­ trol problems with control and trajectory constraints is much easier in terms of optimality functions than in the classical manner. In addition the relationship between optimality conditions for finite dimensional problems and semi infinite optimization and optimal control problems becomes transparent.