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p + 1;\n return result;\n}\n\n// predicted.minFunctionValue = 0;\n// predicted.optima[0] = [0, 0]; This are the points where the function has minimum value\n```\n\n

\n \n

\n\n

\n A tool for global optimization of real valued functions .\n

\n\n## Installation\n\n`$ npm i ml-direct`\n\n## Usage\n\n```js\nimport direct from 'ml-direct';\n\nconst options = {\n iterations: 25,\n};\n\n// for x we explore values between -5 and 4\n// for y we explore values between -2 and 3\n\nconst lowerBoundaries = [-5, -2];\nconst upperBoundaries = [4, 3];\n\nconst quadratic = function (parameters) {\n let [x, y] = parameters;\n return Math.pow(x, 2) + Math.pow(y, 2);\n};\n\nconst predicted = direct(quadratic, lowerBoundaries, upperBoundaries, options);\n\n// predicted.minFunctionValue = 0;\n// predicted.optima[0] = [0, 0];\n```\n\n## [API Documentation](https://mljs.github.io/direct/)\n\n## References\n\n- Jones, D. R., Perttunen, C. D., & Stuckman, B. E. (1993). Lipschitzian optimization without the Lipschitz constant. Journal of optimization Theory and Applications, 79(1), 157-181.\n\n- Björkman, M., & Holmström, K. (1999). Global optimization using the DIRECT algorithm in Matlab.\n\n- Preparata, F. P., & Shamos, M. I. (2012). Computational geometry: an introduction. Springer Science & Business Media.\n\n## License\n\n[MIT](./LICENSE)\n\n[npm-image]: https://img.shields.io/npm/v/ml-direct.svg\n[npm-url]: https://www.npmjs.com/package/ml-direct\n[ci-image]: https://github.com/mljs/direct/workflows/Node.js%20CI/badge.svg?branch=main\n[ci-url]: https://github.com/mljs/direct/actions?query=workflow%3A%22Node.js+CI%22\n[download-image]: https://img.shields.io/npm/dm/ml-direct.svg\n[download-url]: https://www.npmjs.com/package/ml-direct\n","readmeFilename":"README.md"}