This document contains a general overview in the first few sections as well as a more detailed reference in later sections for SVMpython. If you re already familiar with SVMpython, it s possible to get a pretty good idea of how to use the package merely by browsing through svmstruct.py and multiclass.py. This document provides a more in depth view of how to use the package.
Note that this is not a conversion of SVMstruct to Python. It is merely an embedding of Python in existing C code. All code other than the user implemented API functions is still in C, including OPTIMIZATION.
The concept of Adaptive Memory coupled with advances in neighborhood structures
derived from dynamic and adaptive search constructions have been the
source of numerous important developments in metaheuristic OPTIMIZATION
throughout the last decade.
To increase simulation speed, ModelSim® can apply a variety of OPTIMIZATIONs to your design. These include, but are not limited to, mergingprocesses, pulling constants out of loops, clock suppression, and signal collapsing. You control the level of OPTIMIZATION by specifying certain switches when you invoke the compiler.
This a translation of the ToyFDTD c code available from the web site
http://www.borg.umn.edu/toyfdtd/ToyFDTD1.html
This some OPTIMIZATION to use MATLAB matrix notation. Others may find a way to further optimize the nested loops.
Traveling Salesman Problem (TSP) has been an interesting problem for a long
time in classical OPTIMIZATION techniques which are based on linear and nonlinear
programming. TSP can be described as follows: Given a number of cities to visit
and their distances from all other cities know, an optimal travel route has to be
found so that each city is visited one and only once with the least possible distance
traveled. This is a simple problem with handful of cities but becomes complicated
as the number increases.
The market for miniature computer programming is exploding. C++ Footprint and Performance OPTIMIZATION supplies programmers the knowledge they need to write code for the increasing number of hand-held devices, wearable computers, and intelligent appliances.
This book gives readers valuable knowledge and programming techniques that are not currently part of traditional programming training.
In the world of C++ programming, all other things being equal, programs that are smaller and faster are better.
C++ Footprint and Performance OPTIMIZATION contains case studies and sample code to give readers concrete examples and proven solutions to problems that don t have cut and paste solutions.
A .zip file contains a series of scripts that were used in the MathWorks webinar "Using MATLAB to Develop Portfolio OPTIMIZATION Models." The scripts generate 3D efficient frontiers for a universe of 44 stocks with time as the third axis. Additional scripts perform various ex-ante and ex-post analyses. Results are generated with and without market adjustments in the data. A readme.txt. file in the .zip folder describes each script and how to use it
The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants.
The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks OPTIMIZATION Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France).
The version 2.0 (February 2005) handles sparse matrices and contains an example
