when . This last requirement ensures that the function is convex. For such convex quadratic functions, as for any convex functions, any local minimum is global. In fact, when , then the unique minimizer is .
It turns out one can leverage the approach to minimizing more general functions, using an iterative algorithm, based on a local quadratic approximation of the the function at the current point. The approach can then be extended to problems with constraints, by replacing the original constrained problem with an unconstrained one, in which the constraints are penalized in the objective.
We consider an unconstrained minimization problem, where we seek to minimize a function twice-differentiable function . Let us assume that the function under consideration is strictly convex, which is to say that its Hessian is positive definite everywhere. (If is not convex, we might run into a local minima.)
For minimizing convex functions, an iterative procedure could be based on a simple quadratic approximation procedure known as Newton's method. We start with initial guess . At each step , we update our current guess by minimizing the second-order approximation of at , which is the quadratic function (see here)
where denotes the gradient, and the Hessian, of at . Our next guess , will be set to be a solution to the problem of minimizing . Since the function is strictly convex, we have , so that the problem we are solving at each step has a unique solution, which corresponds to the global minimum of . The basic Newton iteration is thus
Two initial steps of Newton's method to minimize the function with domain the whole , and values
A first local quadratic approximation at the initial point is formed (dotted line in green). The corresponding minimizer is the new iterate, . The Newton algorithm proceeds to form a new quadratic approximation of the function at that point (dotted line in red), leading to the second iterate, . Although turns out to be further away from the global minimizer (in light blue), is closer, and the method actually converges quickly.
This idea will fail for general (non-convex) functions. It might even fail for some convex functions. However, for a large class of convex functions, knwon as self-concordant functions, a variation on the Newton method works extremely well, and is guaranteed to find the global minimizer of the function . For such functions, the Hessian does not vary too fast, which turns out to be a crucial ingredient for the success of Newton's method.
Failure of the Newton method to minimize the above convex function. The initial point is chosen too far away from the global minimizer , in a region where the function is almost linear. As a result, the quadratic approximation is almost a straight line, and the Hessian is close to zero, sending the first iterate of Newton's method to a relatively large negative value. The method quickly diverges in this case, with a second iterate at .
The method above can be applied to the more general context of convex optimization problems of standard form:
where every function involved is twice-differentiable, and convex.
The basic idea behind interior-point methods is to replace the constrained problem by an unconstrained one, involving a function that is constructed with the original problem functions. One further idea is to use a logarithmic barrier: in lieu of the original problem, we address
where is a small parameter. The function turns out to be convex, as long as are.
For large, solving the above problem results in a point well ‘‘inside’’ the feasible set, an ‘‘interior point’’. As the solution converges to a global minimizer for the original, constrained problem. In fact, the theory of convex optimization says that if we set , then a minimizer to the above function is -suboptimal. In practice, algorithms do not set the value of so aggressively, and update the value of a few times.
For a large class of convex optimization problems, the function is self-concordant, so that we can safely apply Newton's method to the minimization of the above function. In fact, for a large class of convex optimization problems, the method converges in time polynomial in .
The interior-point approach is limited by the need to form the gradient and Hessian of the function above. For extremely large-scale problems, this task may be too daunting.
Perhaps the simplest algorithm to minimizing a convex function involves the iteration
where is a parameter. The interpretation of the algorithm is that it tries to decrease the value of the function by taking a step in the direction of the negative gradient. For small enough value of , indeed we have .
Depending on the choice of the parameter (as as function of the iteration number ), and some properties on the function , convergence can be rigorously proven.
where is the projection operator, which to its argument associates the point closest (in Euclidean norm sense) to in . Depending on problem structure, this projection may or may not be easy to perform.
Example: In the "sign-constrained least-squares" problem:
the above algorithm involves the iterations