What is the major limitation of steepest descent method?

What is the major limitation of steepest descent method?

The main observation is that the steepest descent direction can be used with a different step size than the classical method that can substantially improve the convergence. One disadvantage however is the lack of monotone convergence.

What is the steepest descent direction?

A steepest descent algorithm would be an algorithm which follows the above update rule, where at each iteration, the direction ∆x(k) is the steepest direction we can take. That is, the algorithm continues its search in the direction which will minimize the value of function, given the current point.

What do you mean by steepest descent?

In mathematics, the method of steepest descent or saddle-point method is an extension of Laplace’s method for approximating an integral, where one deforms a contour integral in the complex plane to pass near a stationary point (saddle point), in roughly the direction of steepest descent or stationary phase.

How do you calculate steepest descent method?

Set φk(t) = f(x(k) − t∇f(x(k))). That is, φk evaluates f along the line through x(k) in the direction of steepest descent. x(k+1) = x(k) − tk∇f(x(k)). ∇f(x, y) = [ 8x − 4y −4x + 4y ] .

Why steepest descent method is useful in unconstrained optimization?

Steepest descent is one of the simplest minimization methods for unconstrained optimization. Since it uses the negative gradient as its search direction, it is known also as the gradient method.

What are the drawbacks of gradient descent method?


  • Can veer off in the wrong direction due to frequent updates.
  • Lose the benefits of vectorization since we process one observation per time.
  • Frequent updates are computationally expensive due to using all resources for processing one training sample at a time.

Is steepest descent gradient descent?

In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function.

How will you calculate the direction of steepest gradient descent along the error surface?

Steepest-Descent Algorithm

  • Estimate a starting design x(0) and set the iteration counter k=0.
  • Calculate the gradient of f(x) at the current point x(k) as c(k)=∇f(x(k)).
  • Calculate the length of c(k) as ||c(k)||.
  • Let the search direction at the current point x(k) be d(k)=−c(k).

Is steepest descent a negative gradient?

While a derivative can be defined on functions of a single variable, for functions of several variables. Since descent is negative sloped, and to perform gradient descent, we are minimizing error, then maximum steepness is the most negative slope.

How does the Conjugate Gradient Method differ from the steepest descent method?

It is shown that the Conjugate gradient method needs fewer iterations and has more efficiency than the Steepest descent method. On the other hand, the Steepest descent method converges a function in less time than the Conjugate gradient method.

What are some of the problems of gradient descent?

The problem with gradient descent is that the weight update at a moment (t) is governed by the learning rate and gradient at that moment only. It doesn’t take into account the past steps taken while traversing the cost space.

What is the main drawback when using the gradient descent algorithm in higher dimensions?

The main disadvantages: It won’t converge. On each iteration, the learning step may go back and forth due to the noise. Therefore, it wanders around the minimum region but never converges.

Is steepest descent same as gradient descent?

In mathematics gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function.

Why is steepest descent slow?

In fact, the steepest descent is typically slow once the local minimization is near. This is because near the local minimization the gradient is nearly zero, and thus the rate of descent is also slow. If high accuracy is needed near the local minimum, other local search methods should be used.

Why conjugate gradient method is better than steepest descent?

It is shown here that the conjugate-gradient algorithm is actually superior to the steepest-descent algorithm in that, in the generic case, at each iteration it yields a lower cost than does the steepest-descent algorithm, when both start at the same point.

What is the drawback of gradient descent approach?

Disadvantages of gradient descent: Can be very, very slow. The direction is not well-scaled. Therefore the number of iterations largely depends on the scale of the problem.

Is steepest descent a conjugate gradient?