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Techniques and principles of minimax theory play a key role in many areas of research, including game theory, optimization, and computational complexity. The classical minimax theorem of von Neumann is a result of this type. Duality theory in linear and convex quadratic programming interprets minimax theory in a different way. The second issue concerns the establishment of sufficient and necessary conditions for values of the variables x and y that achieve the global minimax function value f(x*, y*) = minmaxf(x, y). There are two developments in minimax theory that we would like to mention.
Front Matter
Minimax Theorems and Their Proofs
A Survey on Minimax Trees And Associated Algorithms
An Iterative Method for the Minimax Problem
A Dual and Interior Point Approach to Solve Convex Min-Max Problems
Determining the Performance Ratio of Algorithm Multifit for Scheduling
A Study of On-Line Scheduling Two-Stage Shops
Maxmin Formulation of the Apportionments of Seats to a Parliament
On Shortest K -Edge Connected Steiner Networks with Rectilinear Distance
Mutually Repellant Sampling
Geometry and Local Optimality Conditions for Bilevel Programs with Quadratic Strictly Convex Lower Levels
The Spherical One-Center Problem
On Min-Max Optimization of a Collection of Classical Discrete Optimization Problems
Heilbronn Problem for Six Points in a Planar Convex Body
Heilbronn Problem for Seven Points in a Planar Convex Body
On the Complexity of Min-Max Optimization Problems and their Approximation
A Competitive Algorithm for the Counterfeit Coin Problem
A Minimax αβ Relaxation for Global Optimization
Minimax Problems in Combinatorial Optimization
Back Matter