algorithm-design

Algorithm Design Formalize methods into algorithm pseudocode and system architecture diagrams. Input $0 — Method description or implementation to formalize References Algorithm and diagram templates: ~/.claude/skills/algorithm-design/references/algorithm-templates.md Workflow Step 1: Formalize the Algorithm Define clear inputs and outputs Identify the main loop / recursive structure Specify all parameters and their types Write step-by-step pseudocode Step 2: Generate LaTeX Pseudocode Use algorithm + algpseudocode environments: \begin { algorithm } [ t ] \caption { Method Name } \label { alg:method } \begin { algorithmic } [ 1 ] \Require Input $x$ , parameters $ \theta $ \Ensure Output $y$ \State Initialize ... \For { $t = 1$ to $T$ } \State $z_t \gets f(x_t; \theta )$ \If { convergence criterion met } \State \textbf { break } \EndIf \EndFor \State \Return $y$ \end { algorithmic } \end { algorithm } Step 3: Generate UML Diagrams (Mermaid) Class Diagram classDiagram class Model { +forward (x: Tensor) Tensor +train_step (batch) float } Sequence Diagram sequenceDiagram participant M as Main participant D as DataLoader M ->> D : load_data ( ) D -->> M : batches Step 4: Verify Consistency Every pseudocode step must map to a code module Every class in the UML must exist in the implementation Parameter names must match between pseudocode and code Rules Use standard algorithmic notation (not code syntax) Number lines for easy reference Include complexity analysis as a comment or proposition Use \Require / \Ensure for inputs/outputs Keep pseudocode at the right abstraction level — not too detailed, not too vague