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As applications become more complicated and more demanding
we are witnessing an increasing need for algorithms that can
handle large optimization problems with a large number of inequality
constraints. The standard (classical) approaches are not serving
us well. A new methodology for inequality constraints, referred
to as interior-point methodology, has recently appeared on the
computational methods horizon. In this talk the speaker will
give a brief survey of the standard approaches and point out
their short-comings. The new interior-point methodology will
then be discussed.
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