Differentiable optimization and equation solving : a by J L Nazareth

By J L Nazareth

In 1984, N. Karmarkar released a seminal paper on algorithmic linear programming. through the next decade, it encouraged an incredible outpouring of recent algorithmic effects through researchers world-wide in lots of components of mathematical programming and numerical computation. This ebook provides an outline of the ensuing, dramatic reorganization that has happened in a single of those parts: algorithmic differentiable optimization Read more...

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Additional info for Differentiable optimization and equation solving : a treatise on algorithmic science and the Karmarkar revolution

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The last column identifies each algorithm, and the symbol ∗ indicates that it is new. , Hk = Hk + and M+ k = Mk , and comparison with a more standard implementation of the Newton algorithm. , + Hk = Mk and M+ k = Mk , and comparison of its performance with the standard SR1 quasi-Newton and BFGS variable-metric algorithms. An implementation based on a model–metric combination of the two 24 2. 2 Examples of NC algorithms. updates could very well prove to be more effective, in practice, than a model-based SR1 or a metric-based BFGS implementation.

The global homotopy is said to be regular at the point µ if Hx (x, µ) is of full rank for all x ∈ H−1 (µ). Furthermore, the global homotopy is said to be boundary-free at the point µ if x ∈ D0 for all x ∈ H−1 (µ). Suppose h is twice continuously differentiable, the set D is compact, and the global homotopy H is regular and boundary-free for all points µ ∈ U , where U = {µ|0 < µ ≤ 1}. These conditions are sufficient to ensure the existence, via the implicit function theorem, of a unique differentiable path of solutions (x(µ), µ) that leads from the initial point (x(0) , 1) to a solution of the original system (x∗ , 0).

The principal challenge of algorithmic optimization is to find a solution with as few requests for information as possible. Thus, a much better metaphor, again for the case n = 2, is that of a small boat floating on an opaque lake that entirely covers the landscape. At the current location of the boat, the depth of the lake, the slope, and perhaps even the curvature can be obtained, each at a known cost. The boatman’s task is to use the information that he chooses to gather to move the boat across the surface to a new and better location, where the landscape can be sampled again.

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