Introduction & Installation

Welcome to WaSHI (WebAssembly-backed, Solver-independent, High-level modeling Interface).

WaSHI brings the expressive elegance of Python's PuLP and Julia's JuMP to the modern TypeScript and JavaScript ecosystem. You can formulate mathematical optimization problems using natural algebraic expressions and solve them natively using industrial WebAssembly solvers (HiGHS, Z3, and GLPK)—with zero native C/C++ compilation steps.

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📦 Installation

Install WaSHI using your preferred package manager:

npm

npm install @ohtaman/washi

pnpm

pnpm add @ohtaman/washi

yarn

yarn add @ohtaman/washi

bun

bun add @ohtaman/washi

Optional Solver Dependencies

WaSHI ships with HiGHS built-in with zero configuration. If you need SMT logic / Unsat Core diagnosis via Z3, or compatibility with GLPK, install their respective peer dependencies:

# For SMT logic and Unsat Core conflict diagnosis
npm install z3-solver

# For GLPK solver (GPL-3.0)
npm install glpk.js

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🚀 Quickstart Walkthrough

Here is a step-by-step example of solving a production allocation problem.

1. Import the Core Classes

import { Model, expr, c, maximize } from "@ohtaman/washi";

2. Instantiate a Model

const model = new Model("WoodworkingProfit");

3. Add Decision Variables

Variables represent the unknowns you want to determine. Factory methods (m.continuous, m.integer, m.binary) clearly define their domain:

const x = model.integer("x", { lb: 0 });
const y = model.integer("y", { lb: 0 });

4. Set the Objective Function

Use the expr tagged template literal along with maximize(...) or minimize(...) to define the goal:

// Maximize 30*x + 50*y
model.objective(maximize(expr`30 * ${x} + 50 * ${y}`));

5. Add Constraints

Use model.constraint() with the c tagged template to formulate equality and inequality constraints:

model.constraint(c`${x} + 2 * ${y} <= 40`, "wood_capacity");
model.constraint(c`2 * ${x} + ${y} <= 50`, "labor_capacity");

6. Solve and Extract Results

Solve the model asynchronously via WebAssembly (HiGHS by default):

const status = await model.solve();

console.log("Status:", status); // "Optimal"
console.log("Optimal Objective Value:", model.objectiveValue); // 1100
console.log(`Solution: x = ${x.value}, y = ${y.value}`); // x = 20, y = 10

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🧭 Next Steps

• Learn how to run WaSHI across different environments in Runtime Environments.
• Understand how to formulate complex expressions in the Modeling Guide.
• Explore built-in and pluggable solvers in the Solvers Matrix.