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An interior point method was discovered by Soviet mathematician I. I. Dikin in 1967. [1] The method was reinvented in the U.S. in the mid-1980s. In 1984, Narendra Karmarkar developed a method for linear programming called Karmarkar's algorithm, [2] which runs in provably polynomial time (() operations on L-bit numbers, where n is the number of variables and constants), and is also very ...
Originally described in Xu's Ph.D. thesis [9] and later published in Bramble-Pasciak-Xu, [10] the BPX-preconditioner is one of the two major multigrid approaches (the other is the classic multigrid algorithm such as V-cycle) for solving large-scale algebraic systems that arise from the discretization of models in science and engineering ...
In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables). [1]
A simple example of such a problem is to find the curve of shortest length connecting two points. If there are no constraints, the solution is a straight line between the points. However, if the curve is constrained to lie on a surface in space, then the solution is less obvious, and possibly many solutions may exist.
Solving the general non-convex case is an NP-hard problem. To see this, note that the two constraints x 1 ( x 1 − 1) ≤ 0 and x 1 ( x 1 − 1) ≥ 0 are equivalent to the constraint x 1 ( x 1 − 1) = 0, which is in turn equivalent to the constraint x 1 ∈ {0, 1}.
Each step often involves approximately solving the subproblem (+) where is the current best guess, is a search direction, and is the step length. The inexact line searches provide an efficient way of computing an acceptable step length that reduces the objective function 'sufficiently', rather than minimizing the objective function over + exactly.
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In this example, the first line defines the function to be minimized (called the objective function, loss function, or cost function). The second and third lines define two constraints, the first of which is an inequality constraint and the second of which is an equality constraint.