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Topic:
find minimum of a function with abs and squares analytically
Replies:
3
Last Post:
Dec 23, 2012 5:19 PM




Re: find minimum of a function with abs and squares analytically
Posted:
Dec 23, 2012 4:42 PM


On Sunday, December 23, 2012 9:08:56 AM UTC8, richard...@gmail.com wrote: > Hi, > > > > I want to find the analytical minimum 'x_opt=argmin(x)' of the following function: > > > > f(x) = alpha * c + x + beta * x ^ 2 > > > > where x is a real number (x is_element_of R), c is a real constant (c is_element_of R), alpha and beta are positive real constants (alpha is_element_of R+), beta is_element_of R+), ? is the absolute value function and ^ is the power function. > > > > Looks simple, but the absolute value function makes it somewhat tricky. As already mentioned, i want to find the solution to this minimization problem analytically, not numerically. > > > > I managed to split the optimization up according to the three cases x < c, x = c, x > c, and solve each case separately analytically (by setting the first derivative to zero). > > > > For the three cases I have now the solutions x_opt = alpha/(2*beta) [for x < c], x_opt = c [for x = c], and x_opt = alpha/(2*beta) [for x > c]. But how to 'combine' these solutions now to get the solution 'x_opt' (as a function of 'x') ? > > > > So i would need a function 'phi(x)' which delivers me 'x_opt' for a given x, 'phi(x) = argmin f(x)'. How does phi(x) look like ? > > > > thx in advance for any advice.
Optimizing in each part separately *by setting the derivative to zero* may lead nowhere. If alpha and beta are both > 0, the optimum in each separate region may be at the endpoint x = c, where the function does not have a derivative at all. So, you should modify the procedure to say: the optimum in the region (for each separate region) is either where the derivative = 0, or is at the endpoint. The righthand end x = c (for the region {x <= c} is the minimum if f'(c0) <= 0. The lefthand endpoint x = c (for the region {x >= c} is the minimum if f'(+0) >= 0.



