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- Quasiconvex & Quasiconcave functions
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- Algorithms for Convex Problems
Convex Optimization - Minima and Maxima
Local Minima or Minimize
ˉx∈S is said to be local minima of a function f if f(ˉx)≤f(x),∀x∈Nε(ˉx) where Nε(ˉx) means neighbourhood of ˉx, i.e., Nε(ˉx) means $\left \| x-\bar{x} \right \|
Local Maxima or Maximizer
ˉx∈S is said to be local maxima of a function f if f(ˉx)≥f(x),∀x∈Nε(ˉx) where Nε(ˉx) means neighbourhood of ˉx, i.e., Nε(ˉx) means $\left \| x-\bar{x} \right \|
Global minima
ˉx∈S is said to be global minima of a function f if f(ˉx)≤f(x),∀x∈S
Global maxima
ˉx∈S is said to be global maxima of a function f if f(ˉx)≥f(x),∀x∈S
Examples
Step 1 − find the local minima and maxima of f(ˉx)=|x2−4|
Solution −
From the graph of the above function, it is clear that the local minima occurs at x=±2 and local maxima at x=0
Step 2 − find the global minima af the function f(x)=|4x3−3x2+7|
Solution −
From the graph of the above function, it is clear that the global minima occurs at x=−1.