Terminology word list#
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LP#
Linear programming (LP), a mathematical method for finding the best possible outcome, from a linear model.
MIP#
Mixed integer problem (MIP), optimization technique for solving problems with both continuous (real-valued) and discrete (integer or binary) decision variables.
SDP#
Stochastic dynamic programming (SDP), a mathematical optimization technique for sequential decision-making problems where outcomes are affected by random uncertainty over time, aiming to maximize expected future return.
SDDP#
Stochastic dual dynamic programming (SDDP), similar in concept to SDP, but uses sampling to avoid curse of dimensionality.
curse of dimensionality#
When an increase in problem size exponentially increases solution time (or solution space).
MPI#
Message parsing interface (MPI) - framework to communicate across computer processes, used to parallelize work over distributed memory. Can be used across CPUs, computer nodes or computers. Gold standard for distributed memory parallelization.
deterministic#
Having a clear cause-effect relatioship.
stochastic#
Opposed to deterministic, there is an uncertainty present. Etymology: greek, “guess”.
YAML#
File format, file ending .yaml or .yml. .yml pronounced as “yaml”.
Markov model#
A model describing a system which is changing over time but the next state is only dependent on the current state, not previous states.
future cost function#
A function or variable containing the estimate of the cost of operating as planned from a given time and up to a defined future, ex. from today up until end of simulation horizon.
K-cost#
Cost estimate from backward phase of iteration. Upper bound of optimal cost.
F-cost#
Cost estimate from forward phase of iteration. Lower bound of optimal cost.
water value#
The opportunity cost of not using water at a given time and state of reservoirs. Dual value of reservoir volume.
price taker#
A market actor which does not influence the price. Large actors whose actions can influence or control the market price can be price makers.
convex#
For functions, the property that the function is continous and any line between two points on the graph of the function lie on or above the graph; “smiling”. Important property because it guarantees a solution, i.e., the existence of a global minimum which can be found by moving along the function in any direction.
concave#
Opposite to convex.