I have a multivariable function that I have defined the cost function and its gradient with respect to the variable vector. Let's call the cost function f ( x → ), and variable vector x → . I have been using nonlinear conjugate gradient descenet method for minimization. Algorithm is as follows: k = 0 x = 0 g 0 = ∇ x → f ( x 0 ) Δ x 0 = − g 0 x k + 1 ← x k + t Δ x k g k + 1 ← ∇ f ( x k + 1 ) Δ x k + 1 ← − g k + 1 + γ Δ x k I know that I need to update formula so the solution "ascends" instead of descension. How can I update the algorithm? Also I wonder how the wolfe condition changes for maximization problems. Thank you and have a nice day.

Question

I have a multivariable function that I have defined the cost function and its gradient with respect to the variable vector. Let&#039;s call the cost function   f  (      x    →    ), and variable vector       x    →  . I have been using nonlinear conjugate gradient descenet method for minimization. Algorithm is as follows:  k  =  0    x  =  0        g    0    =      ∇                  x        →              f  (      x    0    )    Δ      x    0    =  −      g    0        x          k      +      1        ←      x    k    +  t  Δ      x    k          g          k      +      1        ←  ∇  f  (      x          k      +      1        )    Δ      x          k      +      1        ←  −      g          k      +      1        +  γ  Δ      x    k  I know that I need to update formula so the solution &quot;ascends&quot; instead of descension. How can I update the algorithm? Also I wonder how the wolfe condition changes for maximization problems. Thank you and have a nice day.

Myla Pierce · Accepted Answer

Convert your function from maximization problem to minimization by setting       f          n      e      w        (  x  )  =  −  f  (  x  ) and run default conjugate gradient for minimization.Since you had specifically asked how the formulas and wolfe conditions update you can plug in   −  f  (  x  ) for function values and   −  ∇  f  (  x  ) for gradients and see for yourself.