StudentsEducators

Legendre Polynomial

Legendre Polynomials are a sequence of orthogonal polynomials that arise in solving problems in physics and engineering, particularly in the context of potential theory and quantum mechanics. They are denoted as Pn(x)P_n(x)Pn​(x), where nnn is a non-negative integer, and the polynomials are defined on the interval [−1,1][-1, 1][−1,1]. The Legendre polynomials can be generated using the following recursive relation:

P0(x)=1,P1(x)=x,Pn(x)=(2n−1)xPn−1(x)−(n−1)Pn−2(x)nP_0(x) = 1, \quad P_1(x) = x, \quad P_{n}(x) = \frac{(2n-1)xP_{n-1}(x) - (n-1)P_{n-2}(x)}{n}P0​(x)=1,P1​(x)=x,Pn​(x)=n(2n−1)xPn−1​(x)−(n−1)Pn−2​(x)​

These polynomials have several important properties, including orthogonality:

∫−11Pm(x)Pn(x) dx=0for m≠n\int_{-1}^{1} P_m(x) P_n(x) \, dx = 0 \quad \text{for } m \neq n∫−11​Pm​(x)Pn​(x)dx=0for m=n

Additionally, they satisfy the Legendre differential equation:

(1−x2)d2Pndx2−2xdPndx+n(n+1)Pn=0(1-x^2) \frac{d^2P_n}{dx^2} - 2x \frac{dP_n}{dx} + n(n+1)P_n = 0(1−x2)dx2d2Pn​​−2xdxdPn​​+n(n+1)Pn​=0

Legendre polynomials are widely used in applications such as solving Laplace's equation in spherical coordinates, performing numerical integration (Gauss-Legendre quadrature), and

Other related terms

contact us

Let's get started

Start your personalized study experience with acemate today. Sign up for free and find summaries and mock exams for your university.

logoTurn your courses into an interactive learning experience.
Antong Yin

Antong Yin

Co-Founder & CEO

Jan Tiegges

Jan Tiegges

Co-Founder & CTO

Paul Herman

Paul Herman

Co-Founder & CPO

© 2025 acemate UG (haftungsbeschränkt)  |   Terms and Conditions  |   Privacy Policy  |   Imprint  |   Careers   |  
iconlogo
Log in

Zeeman Splitting

Zeeman Splitting is a phenomenon observed in atomic physics where spectral lines are split into multiple components in the presence of a magnetic field. This effect occurs due to the interaction between the magnetic field and the magnetic dipole moment associated with the angular momentum of electrons in an atom. When an external magnetic field is applied, the energy levels of the atomic states are shifted, leading to the splitting of the spectral lines.

The energy shift can be described by the equation:

ΔE=μB⋅B⋅mj\Delta E = \mu_B \cdot B \cdot m_jΔE=μB​⋅B⋅mj​

where ΔE\Delta EΔE is the energy shift, μB\mu_BμB​ is the Bohr magneton, BBB is the magnetic field strength, and mjm_jmj​ is the magnetic quantum number. The resulting pattern can be classified into two main types: normal Zeeman effect (where the splitting occurs in triplet forms) and anomalous Zeeman effect (which can involve more complex splitting patterns). This phenomenon is crucial for various applications, including magnetic resonance imaging (MRI) and the study of stellar atmospheres.

Borel’S Theorem In Probability

Borel's Theorem is a foundational result in probability theory that establishes the relationship between probability measures and the topology of the underlying space. Specifically, it states that if we have a complete probability space, any countable collection of measurable sets can be approximated by open sets in the Borel σ\sigmaσ-algebra. This theorem is crucial for understanding how probabilities can be assigned to events, especially in the context of continuous random variables.

In simpler terms, Borel's Theorem allows us to work with complex probability distributions by ensuring that we can represent events using simpler, more manageable sets. This is particularly important in applications such as statistical inference and stochastic processes, where we often deal with continuous outcomes. The theorem highlights the significance of measurable sets and their properties in the realm of probability.

Lyapunov Exponent

The Lyapunov Exponent is a measure used in dynamical systems to quantify the rate of separation of infinitesimally close trajectories. It provides insight into the stability of a system, particularly in chaotic dynamics. If two trajectories start close together, the Lyapunov Exponent indicates how quickly the distance between them grows over time. Mathematically, it is defined as:

λ=lim⁡t→∞1tln⁡(d(t)d(0))\lambda = \lim_{t \to \infty} \frac{1}{t} \ln \left( \frac{d(t)}{d(0)} \right)λ=t→∞lim​t1​ln(d(0)d(t)​)

where d(t)d(t)d(t) is the distance between two trajectories at time ttt and d(0)d(0)d(0) is their initial distance. A positive Lyapunov Exponent signifies chaos, indicating that small differences in initial conditions can lead to vastly different outcomes, while a negative exponent suggests stability, where trajectories converge over time. In practical applications, it helps in fields such as meteorology, economics, and engineering to assess the predictability of complex systems.

Giffen Good Empirical Examples

Giffen goods are a fascinating economic phenomenon where an increase in the price of a good leads to an increase in its quantity demanded, defying the basic law of demand. This typically occurs in cases where the good in question is an inferior good, meaning that as consumer income rises, the demand for these goods decreases. A classic empirical example involves staple foods like bread or rice in developing countries.

For instance, during periods of famine or economic hardship, if the price of bread rises, families may find themselves unable to afford more expensive substitutes like meat or vegetables, leading them to buy more bread despite its higher price. This situation can be juxtaposed with the substitution effect and the income effect: the substitution effect encourages consumers to buy cheaper alternatives, but the income effect (being unable to afford those alternatives) can push them back to the Giffen good. Thus, the unique conditions under which Giffen goods operate highlight the complexities of consumer behavior in economic theory.

Hyperinflation Causes

Hyperinflation is an extreme and rapid increase in prices, typically exceeding 50% per month, which erodes the real value of the local currency. The causes of hyperinflation can generally be attributed to several key factors:

  1. Excessive Money Supply: Central banks may print more money to finance government spending, especially during crises. This increase in money supply without a corresponding increase in goods and services leads to inflation.

  2. Demand-Pull Inflation: When demand for goods and services outstrips supply, prices rise. This can occur in situations where consumer confidence is high and spending increases dramatically.

  3. Cost-Push Factors: Increases in production costs, such as wages and raw materials, can lead producers to raise prices to maintain profit margins. This can trigger a cycle of rising costs and prices.

  4. Loss of Confidence: When people lose faith in the stability of a currency, they may rush to spend it before it loses further value, exacerbating inflation. This is often seen in political instability or economic mismanagement.

Ultimately, hyperinflation results from a combination of these factors, leading to a vicious cycle that can devastate an economy if not addressed swiftly and effectively.

Market Failure

Market failure occurs when the allocation of goods and services by a free market is not efficient, leading to a net loss of economic value. This situation often arises due to various reasons, including externalities, public goods, monopolies, and information asymmetries. For example, when the production or consumption of a good affects third parties who are not involved in the transaction, such as pollution from a factory impacting nearby residents, this is known as a negative externality. In such cases, the market fails to account for the social costs, resulting in overproduction. Conversely, public goods, like national defense, are non-excludable and non-rivalrous, meaning that individuals cannot be effectively excluded from their use, leading to underproduction if left solely to the market. Addressing market failures often requires government intervention to promote efficiency and equity in the economy.