// multi-utility computation suite · offline · instant · precise
┌──────────────────────────┐
│ [c] calcalyst_ │
│ computation suite │
└──────────────────────────┘
// select a module to initialize
/ search↵ open firstesc close
// adsenseEMPTY_LEADER_SLOT728×90
// adsenseMOBILE_ANCHOR_SLOT320×50
// keyboard_shortcuts
/focus search
↑↓navigate module list
Enter
open first result from search
open highlighted
compute when module is open
compute when focused in a field
Escclose module · clear selection
⌫
math.partial-derivatives Calculator
Calculates partial derivatives of a multivariable polynomial function with respect to x and y, and evaluates the gradient vector at a point. The gradient vector points in the direction of steepest ascent — the foundation of gradient descent in machine learning.
Inputs
X
Reference formula or conversion factor shown for context.
Y
Reference formula or conversion factor shown for context.
A
Reference formula or conversion factor shown for context.
B
Reference formula or conversion factor shown for context.
Results
f(x,y) = ax^2+bxy+y^2
Reference formula or conversion factor shown for context.
df/dx
Reference formula or conversion factor shown for context.
df/dy
Reference formula or conversion factor shown for context.
gradient magnitude
Sample size or count used in the calculation.
Hessian determinant
A single number summarising key properties of a matrix. Non-zero: the system has a unique solution. Zero (singular matrix): no unique solution exists.
critical point type
The classification or type assigned based on the inputs.