An algorithm is a sequence of instructions to perform an action.

The energy complexity of an algorithm is the specific amount of energy it requires to execute. It depends on the number of elementary operations performed (time complexity) and the hardware's efficiency. Algorithms with lower time complexity generally consume less energy because they perform fewer operations.

Some ways to reduce energy complexity in algorithms and data structures

• Using algorithms with lower time complexity whenever possible. For example, in graph processing, Fibonacci heap implementations (O(E + V log V)) have lower time complexy than Dijkstra's algorithm (O(V²)).

• Using approximation algorithms when exact solutions are computationally expensive and not necessary. For example, approximate nearest-neighbour algorithms can reduce energy complexity in high-dimensional data searches.

• Reducing precision in machine learning tasks, such as using 16-bit floating-point operations instead of 32-bit.

• Replacing linked lists with dynamic arrays for random access operations.

• Using compact data structures, such as succinct trees or bloom filters.