Complex Numbers

Complex numbers extend the real number system to solve equations that have no real solutions, particularly those involving square roots of negative numbers. The imaginary unit i is defined as the square root of −1, allowing equations like x² + 1 = 0 to have solutions within the complex number system. This expansion ensures all polynomial equations can be solved.

Complex numbers are added and subtracted by combining their real and imaginary parts separately. For example, to add (x + yi) and (u + vi), add the real parts (x + u) and the imaginary parts (y + v) to get (x + u) + (y + v)i. Subtraction follows the same process, subtracting corresponding parts independently.

Multiplying complex numbers uses the distributive property, treating i as a variable. The key simplification rule is i² = −1, which replaces any occurrence of i² with −1 during simplification. For example, (2 + 3i)(1 + i) = 2 + 2i + 3i + 3i² = 2 + 5i − 3 = −1 + 5i.

To divide complex numbers, multiply both the numerator and denominator by the conjugate of the denominator. The conjugate of (u + vi) is (u − vi). This eliminates the imaginary part from the denominator, leaving a real number that allows simplification of the entire fraction.

The complex conjugate of (x + yi) is (x − yi), with the same real part and opposite imaginary part. When a complex number is multiplied by its conjugate, the result is always a real number. This property is essential for division and simplification, as it converts complex denominators into real numbers.

Complex numbers are visualized on the complex plane, where the real part is plotted on the horizontal axis and the imaginary part on the vertical axis. Each complex number corresponds to a unique point on this plane, with perpendicular axes representing the real and imaginary dimensions.

Complex numbers are used in electrical engineering to express impedance in alternating current circuits, where the imaginary part represents reactance. In signal processing and control systems, they model oscillations, phase shifts, and frequency responses. Medical imaging, such as Magnetic Resonance Imaging, uses complex signal data called k-space, converted into spatial images using inverse Fourier transforms.