Many real-world optimization problems have constraints - for example, a
Local minima, often heuristics such as multiple random starts must beĪdopted to find a “good” enouhg solution.Īnother example of constrained optimization ¶ Are there any constraints that the solution must meet?įinally, we need to realize that optimization mehthods are nearly alwaysĭesigned to find local optima.
ToĬhoose an appropirate optimization algorihtm, we should at least answr Regression, the function that we are optimizing is of the form The function as a mathemtical expresssion. To find the minimum of a function, we first need to be able to express Integer-valued) are outside the scope of this course. Or real-valued smooth functions - non-smooth or discrete functions (e.g. We will also assume that we are dealing with multivariate Of generality, since to find the maximum, we can simply minime Univariate or multivariate function \(f(x)\). We will assume that our optimization problem is to minimize some
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