Integral Global Optimization

I Preliminary.- §1 Introduction.- §2 An Appropriate Concept of Measure.- II Integral Characterizations of Global Optimality.- §1 Mean Value Conditions.- §2 Variance and Higher Moment Conditions.- §3 The Constrained Cases.- §4 Penalty Global Optimality Conditions.- §5 Convex Programming.- §6 Optimality Conditions for Differentiable Functions.- §7 Integer and Mixed Programming.- §8 Optimality Conditions for a Class of Discontinuous Functions.- III Theoretical Algorithms and Techniques.- §1 The Mean Value-Level Set (M-L) Method.- §2 The Rejection and Reduction Methods.- §3 Global SUMT and Discontinuous Penalty Functions.- §4 The Nonsequential Penalty Method.- §5 The Technique of Adaptive Change of Search Domain.- §6 Stability of Global Minimization.- 6.1 Continuity of Mean Value.- 6.2 Stability of Global Minima.- §7 Lower Dimensional Approximation.- IV Monte Carlo Implementation.- §1 A Simple Model of Implemention.- §2 Statistical Analysis of the Simple Model.- §3 Strategies of Adaptive Change of Search Domains.- §4 Remarks on Other Models.- §5 Numerical Tests.- V Applications.- §1 Unconstrained Problems.- §2 Applications of the Rejection Method.- §3 Applications of the Reduction Method.- §4 An Application of the Penalty Method.- §5 An Application of Integer and Mixed Programming.