API Features

Comprehensive API Capabilities

Inspect complete JSON outputs, including metadata, computation steps, structured results, among many others.

Metadata

Request tracking, usage, and response information

Status

Success status codes with detailed request tracking

Request

Request ID, correlation ID, processing time in milliseconds and input hash for caching

Usage Tracking

Plan details, request limits, and remaining usage monitoring

API Versioning

Version and schema tracking with deprecation warnings

Response size

Original size in bytes and gzip compression with ratio tracking for optimized bandwidth

Taxonomy

Hierarchical organization of mathematical concepts, operations, and relationships. The example below demonstrates the taxonomy structure for matrix subtraction.

Domain

Mathematics

Branch

Linear Algebra

Module

Matrices

Topic

Arithmetic

Concept

Matrix Subtraction

Concept Slug

subtract

Task

Task to be performed. Parsed from the user's input as a concrete, executable action.

Endpoint

API route configuration and description

POST/v1/mathematics/linear-algebra/matrices/arithmetic/subtract
Compute the difference of two matrices.

Normalized Request

Structured input parameters with type validation

Input Parameters

Matrix A and Matrix B with their values and LaTeX representations

2 parameters

Options

Response profile, expression evaluation, decimals, and more

5 parameters

Response Controls

Control which sections are included in the response

3 parameters

Intermediate

Partial computation results (particular tables) and state snapshots from each stage of the processing pipeline

Result

Computed output with multiple representations

Matrix Answer

[[9, 3, 0], [-1, -3, 8], [-1, 7, 3]]

Matrix Properties

Kind: matrix

Shape: 3 × 3

Size: 72 bytes

Plain Text

[[9, 3, 0], [-1, -3, 8], [-1, 7, 3]]

LaTeX

\( \color{blue}{M = \left[\begin{array}{rrr} 9 & 3 & 0 \\ -1 & -3 & 8 \\ -1 & 7 & 3 \end{array}\right]} \)

CSV

9,3,0\r\n-1,-3,8\r\n-1,7,3

Step-by-Step Solutions

Detailed walkthrough of the problem-solving process

1

Background Theory

Mathematical foundation and concept explanation

2

Input Matrices

Identification and formatting of input data

3

Computation

Step-by-step calculation with explanations

4

Final Answer

Result with verification and validation

Each step includes LaTeX formatting, explanations, and contextual information

Quick Actions

Related operations available at your fingertips

Determinant 2 x 2

Compute the determinant of a 2x2 matrix

Determinant 3 x 3 - Sarrus

Compute determinant using the Sarrus rule

Determinant n x n - Gauss

Compute determinant using Gaussian elimination

Explore

Discover related operations and extensions

Dimensions

3 operations

Statistics

15 operations

Transformations

6 operations

Square Operations

4 operations

Matrix Forms

2 operations

Learn

Comprehensive educational content and resources

Summary

Concise explanation of the concept

Prerequisites

Required knowledge before learning

Objectives

Learning goals and outcomes

Misconceptions

Common errors and corrections

A-Level
~30 min solve time
Easy difficulty
12+ tags

Implementations

Multi-language code examples and libraries

Python - NumPy

Available

Python - SymPy

Available

MATLAB

Available

R

Available

JavaScript

Not available

Julia

Not available

Exercises

Practice problems and assessments

Multiple Choice

3 questions with detailed explanations

Easy

Practice Problems

2 problems with solutions

Apply

Application Problems

2 real-world scenarios

Medium

Metadata

Request tracking, usage, and response information

Remarks

Additional notes, comments, or observations about the response

Warnings

Issues or concerns identified in the response that may require attention

Notices

Informational messages about the response or its processing

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