Abstract
Optimization problems with affine linear constraints and an objective function which is the sum of a linear function and a quadratic form with a positive semi-definite matrix, are in an intermediate position between linear and convex optimization problems. On the one hand, they are a special case of convex optimization, and all of the theorems of Chapter II naturally apply. On the other hand, they have certain properties which we recall from linear optimization, and which are no longer found in general convex optimization.KeywordsSimplex MethodFeasible PointDuality TheoremMinimal SolutionConvex Optimization ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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