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=={{header|Typescript}}==
;Translation of :
* [Numerical Methods, Algorithms and Tools in C# by Waldemar Dos Passos (18.2 Gradient Descent Method]
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Revision as of 07:52, 1 July 2019
Gradient descent
You are encouraged to solve this task according to the task description, using any language you may know.
You are encouraged to solve this task according to the task description, using any language you may know.
Typescript
- Translation of
- [Numerical Methods, Algorithms and Tools in C# by Waldemar Dos Passos (18.2 Gradient Descent Method]
<lang Typescript> // Using the steepest-descent method to search // for minimum values of a multi-variable function export const steepestDescent = (x: number[], alpha: number, tolerance: number) => {
let n: number = x.length; // size of input array let h: number = 0.0000006; //Tolerance factor let g0: number = g(x); //Initial estimate of result
//Calculate initial gradient let fi: number[] = [n];
//Calculate initial norm fi = GradG(x, h); // console.log("fi:"+fi);
//Calculate initial norm let DelG: number = 0.0;
for (let i: number = 0; i < n; ++i) { DelG += fi[i] * fi[i]; } DelG = Math.sqrt(DelG); let b: number = alpha / DelG;
//Iterate until value is <= tolerance limit while (DelG > tolerance) { //Calculate next value for (let i = 0; i < n; ++i) { x[i] -= b * fi[i]; } h /= 2;
//Calculate next gradient fi = GradG(x, h); //Calculate next norm DelG = 0; for (let i: number = 0; i < n; ++i) { DelG += fi[i] * fi[i]; }
DelG = Math.sqrt(DelG); b = alpha / DelG;
//Calculate next value let g1: number = g(x);
//Adjust parameter if (g1 > g0) alpha /= 2; else g0 = g1; }
}
// Provides a rough calculation of gradient g(x). export const GradG = (x: number[], h: number) => {
let n: number = x.length; let z: number[] = [n]; let y: number[] = x; let g0: number = g(x);
// console.log("y:" + y);
for (let i = 0; i < n; ++i) { y[i] += h; z[i] = (g(y) - g0) / h; } // console.log("z:"+z); return z;
}
// Method to provide function g(x). export const g = (x: number[]) => {
return (x[0] - 1) * (x[0] - 1) * Math.exp(-x[1] * x[1]) + x[1] * (x[1] + 2) * Math.exp(-2 * x[0] * x[0]);
}
export const gradientDescentMain = () => {
let tolerance: number = 0.0000006; let alpha: number = 0.1; let x: number[] = [2];
//Initial guesses x[0] = 0.1; //of location of minimums x[1] = -1; steepestDescent(x, alpha, tolerance);
console.log("Testing steepest descent method"); console.log("The minimum is at x[0] = " + x[0] + ", x[1] = " + x[1]); // console.log("");
}
gradientDescentMain();
</lang>
- Output:
Testing steepest descent method The minimum is at x[0] = 0.10768224291553158, x[1] = -1.2233090211217854