iterative

These can be done with iterative algorithms (gradient descent is one such iterative algorithm, conjugate gradient descent would be an improvement of that).

This encouranges rapid creation of some working prototype and the overall process tends to shift towards more iterative development.

Plus, if you don't use an iterative process, you can bill your client for massive change orders.

Of course, very little development is purely iterative or purely waterfall, but rather it's a spectrum.

Keep focus: Learn or do one thing at a time (apply your iterative philosophy on top).

Waterfall style development (non-iterative) is appropriate for well defined problems, where there are few unknowns.

Ipython notebook completely changed the way I do data science, specifically because its iterative/exploratory capabilities are so powerful.

We are a small, collaborative team, guided by a well defined iterative process.

This is the heart of agile / iterative dev, where productivity is measured by what you learn and validate about your startup's premise.

In contrast, where iterative development shines is where there's an external feedback loop somewhere - this is the user discovery that you're referring to.

This is an iterative process as the design engineers watch the assembly.

The reason I said iterative optimization algorithms can be used for regression because linear regression is a quadratic optimization problem.

So where I have trouble understanding the algorithm is when they apply the operator M_x to increase "the amplitude of the truth vector of clauses with maximum number of satisfied clauses using a partial negation and iterative measurement technique" (page 6 ff.).

Iterative optimization algorithms can be used, or random matrix algorithms.