Download A branch-and-bound algorithm for discrete multi-factor by Niu Shu-fen, Wang Guo-xin, Sun Xiao-ling PDF

By Niu Shu-fen, Wang Guo-xin, Sun Xiao-ling

During this paper, a brand new branch-and-bound set of rules in response to the Lagrangian twin rest and non-stop leisure is proposed for discrete multi-factor portfolio choice version with roundlot limit in monetary optimization. This discrete portfolio version is of integer quadratic programming difficulties. The separable constitution of the version is investigated by utilizing Lagrangian rest and twin seek. Computational effects exhibit that the set of rules is in a position to fixing real-world portfolio issues of information from US inventory marketplace and randomly generated try issues of as much as a hundred and twenty securities.

Show description

Read or Download A branch-and-bound algorithm for discrete multi-factor portfolio optimization model PDF

Similar algorithms and data structures books

Combinatorial Optimization: Theory and Algorithms

This accomplished textbook on combinatorial optimization locations targeted emphasis on theoretical effects and algorithms with provably solid functionality, not like heuristics. It has arisen because the foundation of numerous classes on combinatorial optimization and extra exact subject matters at graduate point. It includes whole yet concise proofs, additionally for plenty of deep effects, a few of which didn't seem in a textbook prior to.

The Structure of Style: Algorithmic Approaches to Understanding Manner and Meaning

Variety is a primary and ubiquitous element of the human event: every person immediately and consistently assesses humans and issues in accordance with their person kinds, teachers identify careers by means of studying musical, inventive, or architectural kinds, and full industries retain themselves via constantly growing and advertising and marketing new kinds.

Handbook of Solubility Data for Pharmaceuticals

Aqueous solubility is without doubt one of the significant demanding situations within the early phases of drug discovery. essentially the most universal and potent tools for reinforcing solubility is the addition of an natural solvent to the aqueous resolution. in addition to an advent to cosolvency types, the instruction manual of Solubility info for prescription drugs offers an intensive database of solubility for prescription drugs in mono solvents and binary solvents.

Extra info for A branch-and-bound algorithm for discrete multi-factor portfolio optimization model

Sample text

As a rule, random number generators are fragile and need to be treated with respect. It’s difficult to be sure that a particular generator is good without investing an enormous amount of effort in doing the various statistical tests that have been devised. The moral is: do your best to use a good generator, based on the mathematical analysis and the experience of others; just to be sure, examine the numbers to make sure that they “look” random; if anything goes wrong, blame the random number generator!

Once the program is written, the numbers that it will produce can be deduced, so how could they be random? The best we can hope to do is to write programs which produce isequences of numbers having many of the same properties as random numbers. Such numbers are commonly called pseudo-random numbers: they’re not really random, but they can be useful 33 CHAF’TER 3 as approximations to random numbers, in much the same way that floatingpoint numbers are useful as approximations to real numbers. (Sometimes it’s convenient to make a further distinction: in some situations, a few properties of random numbers are of crucial interest while others are irrelevant.

Interpolation The “inverse” problem to the problem of evaluating a polynomial of degree N at N points simultaneously is the problem of polynomial interpolation: given a set of N points x1 ,x2,. . ,xN and associated values yr,y2,. . ,yN, find the unique polynomial of degree N - 1 which1 has p(Xl)= Yl,P(zz)= Y21 . '(xN) = YN. The interpolation problem is to find the polynomial, given a set of points and values. The evaluation problem is to find the values, given the polynomial and the points. ) The classic solution to the interpolation problem is given by Lagrange’s interpolation formula, which is often used as a proof that a polynomial of degree N - 1 is completely determined by N points: This formula seems formidable at first but is actually quite simple.

Download PDF sample

Rated 5.00 of 5 – based on 16 votes