Signs of Success

What to Look for in Optimization

All optimization solutions are not created equal. Here are four attributes of successful software at Princeton Consultants.

1. Accurate

An accurate recommendation yields high-scoring solutions while following agreed-upon rules and constraints.


  • Quantifying items that are difficult to quantify.
  • Slaying sacred cows and threatening the status quo.
  • Mixing short-term and long-term objectives.

Our Strategies

  • Calibrate against existing decisions—using pairs-trading to find sensitivities.
  • Find the right executive sponsorship.
  • Provide ranked alternatives along an efficient frontier.

2. Clear

A clear recommendation is unambiguous, precise, and concise.


  • Users want to understand why, not just what.
  • The best choice may be very complex.
  • The entire solution may be too large to easily inspect and validate.

Our Strategies

  • Build and install the model incrementally, solving the way employees do at first.
  • Focus on user-interface design on solution validation.
  • Allow the user to easily inspect and edit input data and parameters.

3. Fast

A fast recommendation is: low latency – it uses up-to-date data and gives answers quickly; and rapid throughout – it solves quickly; and scalable – it has the capacity for more.


  • As real-world, non-linear factors are added, the solving time explodes.
  • Data and transaction volumes and constraints keep increasing.
  • The solver must be fast enough to reflect “what if” usage.

Our Strategies

  • Avoid one-size-fits-all software tools.
  • Combine state-of-the-art approaches.
  • Use parallel processing and spare cycles for continuous improvement of the solution.

4. Robust

A robust recommendation is adaptive as conditions change, self-detects faults and failures, and has a low bug count.


  • Market conditions may change rapidly.
  • Simple heuristics often fail when conditions change.
  • Data can be late, missing, or erroneous.

Our Strategies

  • Make real-time data and solution validation part of the core model.
  • Use self-tuning parameter techniques.
  • Mix experienced optimizers in a team setting and avoid solo efforts.