# Nesbitt's Inequality in C++

Nesbitt's i? a mathematicall? expressed inequality relation th?t connects the arithmetic mean and the harmonic mean of three positive numbers ?, b ?nd c. More precisely, it states that the sum of the reciprocals οf the arithmetic means of the pairs of these numbers i? greater th?n or equal to 3⁄2. The inequality theoretically can be represented as an algebraic expression (a/(b+c)) + (b/(a+c)) + (c/(a+b)) ≥ (3/2). This is n?med Nesbitt's Inequality ?nd it is the source of the insigths reg?rd?ng the interact?on between harmoni? ?nd arithmeti? means in the three positive values.

This means that the total of the reciprocals of the arithmetic means of the pairs of numbers is greater than or equal to 3/2.

• The arithmetic mean of two numbers x and y is given by (x+y)/2.
• The harmonic mean of two numbers x and y is given by (2xy/(x+y)).

Based on the above, Nesbitt's Inequality is that when you sum up reciprocals of harmonic means between pairs of numbers, it will be ≥3/2. It was named after Edward Nesbitt- the famous mathematician who largely contributed to mathematics. You can have a function in C++ that implements this Inequality by taking three positive values, computing the first part of it, and then checking whether its value is greater than or equal to 3/2.

## Approach-1: Direct Implementation

The "Direct Implementation" approach in C++ involves writing a function that applies Nesbitt's Inequality logic but does not require templates or use generic programming.

### Program:

Output:

Enter three positive numbers (double): 5 6 8
Nesbitt's inequality holds true for doubles.

Explanation:

• nesbittInequality Template Function:

The template function denoted as nesbittInequality is designed to accept parameters of several numeric types - a T parameter for any given number type represents the latter.

• Input Validation:

The built-in input validation is used in the Function so that if one of the numbers a, b and c involved as an argument in the calculation procedure equals to or less than zero, it achieves detection of this information. If any of them are, an error message is printed to the standard error stream (std::cerr and the Function resolves to false will show an error.

• Calculation of Nesbitt's Inequality:

The left side of Nesbitt's inequality is calculated using the provided formula: (a/(b+c)) + (b/(a+c)) + (c/(a+b)) ≥ 3/2. The right side is set to 3/2.

• Comparison and Result:

The Function measures Nesbitt's Inequality and returns true if the left side is more than or equal to the right, which means its validity. Otherwise, it returns false.

The program begins by requesting the user enter three positives of a double-precision floating point number. Then, it tests Nesbitt's Inequality through the nesbittInequality Function at double type values. Second, it asks the user to enter three positive natural numbers and apply Nesbitt's Inequality using the same Function but integer values.

In conclusion, the program shows how the template function allows varying numeric types without explicitly describing each type.

Complexity Analysis:

Time Complexity Analysis:

The time complexity of the template function mainly depends on arithmetic operations involved in calculating Nesbitt's Inequality. All these operations (addition, division and comparison) remain in O(1). The template function loops over every step once, giving a linear time complexity of O(1) concerning the input size (the numbers a, b and c).

Input Handling in Main Function:

The primary Function calls the user to enter three positive numbers for double and integer types. Input operations usually have a running time scale as the number of characters typed. However, practically, this is often treated to be constant or O(1). The input-handling operations do not impact the total time complexity much.

The program's time complexity mostly depends on arithmetic operations in the template function. Therefore, it has O(1), which means constant complexity.

Space Complexity Analysis:

The template function allocates a constant number of spaces for local variables. The space needed for these variables does not depend on the input size, so they have O(1) time complexity.

Memory space allocated to variables used in the main Function is also constant. Other space may be used for standard input and output; however, this is not internal to the program nor included in a complexity analysis of space.

With a total space complexity of O(1), the program has constant space complexity. Regarding space utilization, the program still has constant space requirements independent of input size.

Function overloading is a C++ technique that allows the definition of multiple functions within one scope but with different parameter lists. Each form of the Function has a variant that deals with a specific parameter type or number of parameters. Regarding Nesbitt's Inequality, Function overloading may be applied to manage various numeric types.

### Program:

Output:

Enter three positive numbers (double): 1 2 3
Nesbitt's inequality holds true for doubles.

Explanation:

The function then calculates Nesbitt's inequality using the formula (a/(b+c)) + (b/(a+c)) + (c/(a+b)) ≥ 3/2 and compares it with 3/2.

• Integer Version (nesbittInequality for int):

The second same-named overloaded Function is to address integer numbers. It verifies the positiveness of input integers and calculates Nesbitt's Inequality like in its double version.

• Ensuring Positivity:

The first and second versions of the Function have a shared input validation step whereby positive numbers are entered. Should any of them be negative or zero, an error message is generated, and the Function returns false.

• Promoting Robustness:

Incorporating input validation makes the code reliable by eliminating invalid inputs from Nesbitt's inequality calculations. Error messages show feedback, leading the user to give valid positive numbers.

• Example Usage in Main Function:

The key Function shows the implementation of overloaded nesbittInequality functions with double and integer types. The users are requested to type three positive numbers for every kind of numeric.

• Clear Output and Interpretation:

The output messages of the program indicate whether or not Nesbitt's Inequality holds for the given numbers and data types. The information users get is the exact validity of the Inequality for doubles and integers.

• Explicit Type Handling:

Although function overloading improves readability and ease, it explicitly distinguishes the logic for different types. This may lead to code redundancy if the core logic is common for different types. Unlike the template-based approaches, functional overloading does not offer a universal solution to handling any numeric types, as each type requires its function definition.

Complexity Analysis:

Time Complexity Analysis:

The concept of function overloading helps provide separate functions for double and integer types, which are both easy to read and maintain.

All the overloaded functions (nesbittInequality for double and int) have parallel execution routes in input validation, calculating Nesbitt's Inequality and making a comparison. The time complexity of each arithmetic operation (addition, division) is constant. Even with varying input sizes, the control flow remains constant, leading to an overall O(1) time complexity for each overloaded Function.

The main Function calls two overloaded functions with O(1) time complexity. The time complexity is O(1), representing constant time.

Space Complexity Analysis:

Input Variables:

The variables defined within the main Function that hold a user-provided value (num1, num2, num3; intNum1,intNum 2 and int Num 3) require constant space despite their input size.

Function Parameters and Local Variables (nesbittInequality): Therefore, The border's situation, r, remained unchanged.

Every Nesbitt Inequality function has parameters(a, b, c) and locale variables (left_ sides right ). These variables work on constant space for each function invocation, regardless of input size.

Template-Free Approach:

While a template-based approach could produce more instances of the functions for different types, Function overloading results in separate functions specific to certain types but no additional instantiations at runtime. Space complexity is constant.

The space used by the program does not change with any input size. The space complexity of the algorithm overall is O(1), which implies constant-space complexity.

## Approach 3: Using Lambda functions:

Lambda functions offer a convenient way to write anonymous code. They are especially valuable for wrapping algorithms or tests. Nesbitt's Inequality describes lambda functions as representing the arithmetic reason for numeric types.

### Program:

Output:

Enter three positive numbers (double): 1 2 3
Nesbitt's inequality holds true for doubles.

Explanation:

The C++ code checks Nesbitt's Inequality for any numeric type by a lambda function. It starts by defining a lambda function called nesbittInequality, which takes three parameters: a, b and c. Inside the lambda function, it initially checks factors that all three input numbers should be positive values. Otherwise, if one of them is not positive, it throws an error message and delivers false. It derives Nesbitt's Inequality using this formula and completes it with the threshold value 3/2. At last, it returns true if the inequality holds; it is false otherwise.

In the main Function, on the one hand, the program presents how to use a lambda function with double-precision floating point numbers and shows its utilization of integer values. It makes the user power in three positive numbers, all for each numeric type. After the input, it invokes the nesbittInequality lambda function with values entered and shows true/false for each numeric type, either Nesbitt's Inequality or

In general, the program is vivid proof of how universal the lambda functions are in verifying Nesbitt's Inequality for all such numeric types and what one can do without separate implementations.

Complexity Analysis:

Time Complexity Analysis:

The time complexity of input operations using std: Time spent on input processing is O(1) for cin; thus, as time complexity depends only upon the size of cin and not itself, neither dependency factor can be called constant; it becomes a variable. Even with different numbers entered by a user, the time for input remains uniform.

Lambda Function Execution:

The lambda function includes mathematical operations like addition, division and comparison. The operations are regarded as having a constant time complexity (O(1)) since their running time does not depend on the size of input values.

The execution of the lambda function is constant and stable for every set of numeric user input. The main Function invokes the lambda function twice, once for double-precision floating-point numbers and again for integers.

Total Time Complexity:

The computational operations of the lambda function are constant-time, and there is a fixed number of invocations; therefore, their time complexity equals O(1) in total. In this way, the execution time does not grow with increasing problems, which works great for different situations.

Space Complexity Analysis:

Input Variables:

The first Function states different variables (for example, num1, num2.,3), which is used to store input values from the user. All these variables need constant memory space irrespective of the numerical values entered. Such variables have space complexity O(1), meaning constant memory usage.

Lambda Function Local Variables:

Local variables (left_side and right_side) are declared for intermediate calculations in the lambda function. Like the input variables, these local variables take up a constant amount of space for each call to the lambda function. The local variables have a space complexity of O(1), contributing to overall efficiency.

Total Space Complexity:

The space utilized by the entire program does not change due to various input sizes. The overall space complexity is O(1), meaning a constant amount of utilized spaces. This constant space complexity is beneficial for scenarios where memory efficiency matters and the program's working memory requirements do not grow with input size.

## Challenges of Nesbitt's Inequality:

Analyzing and using Nesbitt's Inequality as a mathematical patent of three positive numbers connecting the harmonic mean to their arithmetic means can present multiple hurdles.

Complexity in Interpretation:

The first major challenge is understanding Nesbitt's Inequality itself. However, the connection it creates between harmonic and arithmetic mean is not intelligent, unlike in culture; hence, it needs to be properly studied before understanding.

Application in Problem Solving:

Practically speaking, invoking Nesbitt's Inequality into issue settling can be very tasking. The key to understanding how and when Inequality will be useful in generating insights or meaningful solutions might also involve knowledge of mathematical principles.

Handling Non-Positive Numbers:

Inequality for Nesbitt concerns only positive numbers. The application of Inequality is made complex in cases where either non-positive numbers are involved or when the numbers concerned do not have a strict positive character. Special consideration and changes might be required in such instances.

Mathematical Rigor:

It is sometimes difficult to guarantee the correctness of proofs or applications on Nesbitt's Inequality. Inaccurate judgments or computations may cause wrong conclusions/solutions, thus showing the importance of careful attention to detail.

Generalization and Extensions:

Generalizing or extending Nesbitt's Inequality for three positive numbers is not easy, let alone if we increase the numbers to a bigger population or in another situation. Generalizing the meaning but going beyond directly, that is to say, dealing with a greater number of variables or different natures of data, may require novel techniques and a more profound mathematical understanding.

Commanding Nesbitt's Inequality and preventively implementing the same in their mathematical fields requires patience, persistence, and competency built on solid grounds of analytical mastery and problem-solving techniques.