New PDF release: Approximation Methods for Polynomial Optimization: Models,

By Zhening Li, Simai He, Shuzhong Zhang

ISBN-10: 1461439833

ISBN-13: 9781461439837

Polynomial optimization were a sizzling examine subject for the previous few years and its purposes variety from Operations examine, biomedical engineering, funding technology, to quantum mechanics, linear algebra, and sign processing, between many others. during this short the authors talk about a few very important subclasses of polynomial optimization types coming up from numerous functions, with a spotlight on approximations algorithms with assured worst case functionality research. The short offers a transparent view of the elemental principles underlying the layout of such algorithms and the advantages are highlighted by means of illustrative examples exhibiting the potential applications.

This well timed treatise will entice researchers and graduate scholars within the fields of optimization, computational arithmetic, Operations examine, commercial engineering, and desktop technology.

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Additional info for Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications

Sample text

Z¯ k ) is a feasible solution for (T PS¯ ). 8), we have k F(¯z 1 , z¯ 2 , . . , z¯ d ) ≥ τ0 F(¯y 1 , y¯ 2 , . . , y¯ d ) d ≥ 2−d 2− 2 (n + 1)− =2 − 3d 2 − d−2 2 (n + 1) d−2 2 v(T PS¯ ) v(T PS¯). 1, with only a minor modification at Step 3, namely 1 2 d we choose a solution in argmax F β11y , β21y , . . , βd1y , β ∈ Bd , instead of choosing a solution in argmax F β1 y1 /d , β2 y1 /d , . . , βd y1 /d , β ∈ Bd . 1 (to solve (PS¯)) will become clear later. 11) suggests that a simple randomization process will serve the same purpose, especially when d is large.

T. x¯ k ∈ S¯ n+1 , k = 1, 2, . . , d, which is exactly (TS¯ ) as we discussed in Sect. 1. 5, (T PS¯(1)) admits a polynomial-time approximation d−2 algorithm with approximation ratio (n + 1)− 2 . Therefore, for all t > 0, (T PS¯(t)) also admits a polynomial-time approximation algorithm with approximation ratio d−2 (n + 1)− 2 , and v(T PS¯ (t)) = t d v(T PS¯ (1)). 1), we are able to find a feasible solution (¯y1 , y¯ 2 , . . , y¯ d ) of (T PS¯(1)) in polynomial time, such that F(¯y 1 , y¯ 2 , .

2. For the computational complexity, it is similar to its special cases (TS¯ ) and (HS¯ ). It is solvable in polynomial time when d ≤ 2, and is NP-hard when d ≥ 3, which will be shown shortly later. Moreover, when d ≥ 4 and all di (1 ≤ k ≤ s) are even, there is no polynomial-time approximation algorithm with a positive approximation ratio unless P = NP. This can be verified in its simplest case of d = 4 and d1 = d2 = 2 by using a similar argument as in Ling et al. [72]. In fact, the biquadratic optimization model considered in Ling et al.

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Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications by Zhening Li, Simai He, Shuzhong Zhang


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