Documentation: Relocate technical background for linear and logistic

- Reference technical doc from about section
- Replace non-working example with one from tests for logistic
- Add doxygen @background alias
- Relocate technical background
Linear Regression: Clean-up documentation format

Updated linear regression documentation:
- Changed usage to ensure that arguments are presented in a list
- Removed output information from examples
- Miscellanous changes in formatting
Logistic Regression: Documentation updates

Updates:
    - Use <DL> for arguments and table columns
    - Replace @liarg alias with hard-coded <DL> list.
Config: Remove 4.0 from ports list

Greenplum 4.0 is not supported anymore and has been removed from
the port list.
SVD for PCA: SVD sepecially designed for PCA

This implementation of SVD is mainly for PCA to use. It uses Lanczos
iteration method to decompose the original matrix into PBQ^t, where B is
a bidiagonalized matrix. We assume that B can be loaded into the memory.
Since this SVD implementation is for PCA, this is a reasonable
assumption. B is then further decomposed into XSY^t using Eigen's
JacobiSVD function. The final results are U = PX, V = QY and the
singular values S.

This implementation provides svd (for dense matrix), svd_block (also for
dense matrix but faster), svd_sparse (convert a sparse matrix into a
dense one, slower) and svd_sparse_native (directly operate on the sparse
matrix, much faster for really sparse matrices).

The performance on DCA is comparable to R. Actually, it is faster for
large dense matrices.
PCA: Added the PCA feature to MADlib

Pivotal Tracker: 49430109
This merge adds a PCA training feature to MADlib, building off the SVD
added earlier.  While PCA supports computation of the actual principle
components, the projection and scoring features are not included in this
merge.  Those are expected to be included in a later addition.
PCA: Create a function to project new data to PCA output

Pivotal: 49430497

PCA function outputs the principal components of a data matrix.
This commit adds a projection function to project the same (or new)
data to the subspace spanned by the principal components.
PCA Project: Add all hunks missed due to faulty merge
Matrix Op: Fix a small error causing install-check failure
Doc: New format of elastic_net user doc

- Use the new format of user doc
- New example
- Added the doc for the hidden parameter step_decay
Matrix Operations: Fix order in Column to array operation

The column to array functionality to build a matrix from a table
extracted the list of column names from information schema ordered  by
the ordinal position. This order, however, was lost when the column
names were stored in a 'set' instead of a 'list'. This commit fixes
the problem ensuring that the order of the columns is maintained
while building the matrix.
PCA: Ensure mean centered table is output + other fixes

Pivotal: 54329628

[Changes applicable to PCA Train and PCA Project]
1. PCA Training - New output table will be created during PCA training.
    - outputtable_mean. This table is the mean of the input data
    matrix.
2. PCA project - will use this output (mean of the training set) for
    projecting to the test table.
3. PCA project - accepts col-format input data matrix
    (like SVD and PCA_train).
4. Both - Added user documentation to explain how this is
    being done.
5. Changed computation of Eigen values in PCA to divide by sqrt(n-1)
cart: allow BIGINT for rf_train

Jira: madlib-764

Currently, rf_train won't work if the ID field is a BIGINT. I modified
the function of __sample_with_replacement to correctly handle BIGINT
numbers.
Validation: Fix issue with table/column name double quoted

Changes:
- If the user enters a table name that is in upper case without the
double quotes then psql makes it lower case before retrieving the
table. The validation function after this commit does the same.
- If the table name is entered with a double quote then the validation
funtion should take care of the quotes while retrieving the table.

Other changes:
- A bug with the PCA documentation was fixed in this commit
Documentation: Clean PCA docs to reflect all parameters

Changes:
- Information about the mean table is added
- Documentation format updated to make it clearer and consistent with
other modules
- Table of Contents has been added
Linear systems: Various solvers for linear system of equations

Pivotal: 54330114

Implemented the solver for dense and sparse linear systems
Replace the old utilities.validate_args functions
Make new/delete symbols local to libmadlib.so to avoid possible conflicts with outher packages

Pivotal ID: 50703391
Upgrade from v1.0 to v1.1

Pivotal ID: 54330270
Updated gppkg version number