SMLP tutorial

[mdmitry1]

This tutorial has SMLP 3 examples of various complexities:

Example	Variables	Objectives	Constraints	Difficulty
Eggholder	2	1	None	High (multi-modal)
DORA	2	1	1 (circular)	Low (convex)
BNH	2	2	2 (non-linear)	Medium

Each example contains:

1. Reference
2. Push button script
3. Vizualization

[mdmitry1]

Initial revision - please, review

[mdmitry1]

Added explicit mention that SMLP supports black-box functions

[mdmitry1]

1. Added black-box optimization applications reference to README.md
2. Added reference to black box function definition used in the README.md file
3. Added SMLP flow stages description
4. Multiple style improvements

[mdmitry1]

Fixed typo in the file name

[mdmitry1]

Added certify example .

[mdmitry1]

Synch with smlp_python311 branch

[mdmitry1]

Synch with main branch

[mdmitry1]

Merged with main branch.
After merge there is only one file change in README.md.

[mdmitry1]

1. Changed smlp script name to be just smlp.
2. Updated intel test bench location after merge with master
3. Validate results in virtual environment and Docker container

[zurabksmlp]

Suggestions to improve README.md

1. Constraint defining set is comprised of known functions, which are defined by Python expressions --> both the English and description precision desire improvement
2. In future it may be extended to other modes. --> probably can be omitted, this statement is not informative
3. Optimization: model and constraints are used to find objective function(s) minimum considering input constraints --> SMLP computes maximum, to compute a minimum the objective functions are usually negated
4. Handle single and multi-objective optimization --> I think it must be single- and multi-objective .... (added "-" after "singe:).
5. variables: Define input variables (knobs) and output variables --> variables: Define input variables, knobs (parameters), and output variables
6. rad-abs: Radius for absolute perturbation (0.0 = exact values) --> rad-abs: Radius for absolute perturbation of knobs (0.0 = exact values)
7. alpha: Constraint expression using variable labels --> alpha: Constraint expression using input and knob labels

8. Verification --> ### Results' Verification

9. pip install numpy matplotlib pandas seaborn --> These will be SMLP installation, so are not needed here?
10. Intel example is not described. In case it uses beta constraints, than a description can me added after description of alpha constraints (can copy-paste or adapt from SMLP manual).

Most of the above comments are recommendations, some suggest to fix inaccuracies. None are blocking the PR appeal. I will rerun the the tutorial script and then approve.

[zurabksmlp]

Approving this PR.