Hadjisavvas, Nicolas, Martinez-Legaz, Juan E., Penot, Jean-Paul (Eds.)
2001, IX, 410 p.
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A famous saying (due toHerriot)definescultureas "what remainswhen everythingisforgotten ". One couldparaphrase thisdefinitionin statingthat generalizedconvexity iswhat remainswhen convexity has been dropped . Of course, oneexpectsthatsome convexityfeaturesremain.For functions, convexity ofepigraphs(what is above thegraph) is a simplebut strong assumption.It leads tobeautifulpropertiesand to a field initselfcalled convex analysis. In several models, convexity is not presentandintroducing genuine convexityassumptionswouldnotberealistic. A simple extensionof thenotionof convexity consists in requiringthatthe sublevel sets ofthe functionsare convex (recall thata sublevel set offunction a is theportionof thesourcespaceon which thefunctiontakesvalues below a certainlevel).Its first use is usuallyattributed to deFinetti,in 1949. This propertydefinesthe class ofquasiconvexfunctions, which is much larger thanthe class of convex functions: a non decreasingor nonincreasingone variablefunctionis quasiconvex ,as well asanyone-variable functionwhich is nonincreasingon someinterval(-00,a] or(-00,a) and nondecreasingon its complement.Many otherclasses ofgeneralizedconvexfunctionshave been introduced ,often fortheneeds ofvariousapplications: algorithms ,economics, engineering ,management science,multicriteria optimization ,optimalcontrol, statistics .Thus,theyplay animportantrole in severalappliedsciences . A monotonemappingF from aHilbertspace to itself is a mappingfor which the angle between F(x) - F(y) and x- y isacutefor anyx, y. It is well-known thatthegradientof a differentiable convexfunctionis monotone.The class of monotonemappings(and theclass ofmultivaluedmonotoneoperators) has remarkableproperties.This class has beengeneralizedin various direc tions,withapplicationsto partialdifferentialequations ,variationalinequal ities,complementarity problemsand more generally, equilibriumproblems. The classes ofgeneralizedmonotonemappingsare more or lessrelatedto the classes ofgeneralizedfunctionsvia differentiation or subdifferentiation procedures.They are also link edvia severalothermeans.
Invited Papers.- Minimization of the Sum of Several Linear Fractional Functions.- Discrete Higher Order Convex Functions and their Applications.- Cuts and Semidefinite Relaxations for Nonconvex Quadratic Problems.- Contributed Papers.- The Steiner Ratio of L3p.- Normal Cones to Sublevel Sets: An Axiomatic Approach. Applications in Quasiconvexity and Pseudoconvexity.- Multiobjective Programming with ?-convex Functions.- Rufián-Lizana, Pascual Ruiz-Canales Vector Invex N-set Functions and Minmax Programming.- On the Supremum in Quadratic Fractional Programming.- First and Second Order Characterizations of a Class of Pseudoconcave Vector Functions.- New Invexity-Type Conditions in Constrained Optimization.- Stochastic s-(increasing) Convexity.- Fixed Point Theorems, Coincidence Theorems and Variational Inequalities.- Representation of a Polynomial Function as a Difference of Convex Polynomials, with an Application.- Proper Efficiency and Generalized Convexity in Nonsmooth Vector Optimization Problems.- Duality for Fractional Min-max Problems Involving Arcwise Connected and Generalized Arcwise Connected Functions.- Generalized Convexity for Unbounded Sets: The Enlarged Space.- A Note on Minty Variational Inequalities and Generalized Monotonicity.- On Vector Equilibrium and Vector Variational Inequality Problems.- Stochastic Orders Generated by Generalized Convex Functions.- Separation Theorems for Convex Sets and Convex Functions with Invariance Properties.- Convexity and Generalized Convexity Methods for the Study of Hamilton-Jacobi Equations.- Higher-order Monotone Functions and Probability Theory.- Convexity and Decomposability in Multivalued Analysis.- Scalar Characterization of Generalized Quasiconvex Functions.- Optimality and Wolfe Duality for Multiobjective Programming Problems Involving?-set Functions.- Vector Stochastic Optimization Problems.- On Suprema of Abstract Convex and Quasi-convex Hulls.- Specific Numerical Methods for Solving Some Special Max-min Programming Problems Involving Generalized Convex Functions.