#header-inner {background-position: right !important; width: 100% !important;}

## 1/29/14

### How Stitie Machine can help to code Dynamically?

Dynamic programming is about dividing solution into problems then solving them by globally optimal solution (not neccessarily in order of arrival).

While greedy programming is about solving problems step by step, using locally optimal solution, not considering globally optimal solution at a given moment.

'Naive' example:

1. Let's divide problems on: most optimal cpu-speed wise, memory-size wise, and target-function wise.

Functions can be written as character String using mathematical function notation,

for example: String s1 is "f(x1)+f(x2)".

Target function is (any) function, complex or not, that describes how optimal solution is given it's variables (x1 and x2 in above case). These variables can represent available resources, for example amount of liquid in vial and energy used.

2. Stitie space is 3D space. Let's place problems by these keys on 3-axes.

On first axis let's sort problems by cpu usage.

On second axis let's sort problems by memory usage.

On thrid axis user can have problems sorted by single target function, or assigned to their target functions and sorted alphabetically.

3. According to available resources (memory, speed, other) let's take problems to solve from an appropriate axes (coordinates) and position on such.

1. i think really fast computer programs can be written using this methodology, for it combines Algorithmic optimization with Optimalization, including Bottleneck Optimalization and can be written in fast language such as Assembler or C.

2. Ola AH language should be also fast.

3. Above comment about 'Algorithmic optimization' is true for some problems so it can be used here.

Optimalization is Method involving finding best spot in target function, it can be used here often.

Bottleneck Optimization is optimizing bottleneck using other optimalization methods.

Low level language can be applied any time if someone knows such and has time and means to do so (for changes within time constraints might deny this approach).

4. Bottlenecks depend on problems to be solved and should be measured or found during proper development iteration phase.

5. Not everything can be optimized using Optimalization Methods, for example: spread of energy in space can be expressed (described) by a function, but not optimized that way, at least in common sense. We still can analyze properties of such a function.