A **controlled invariant subspace** is a concept from control theory and linear algebra that pertains to the behavior of dynamical systems. In the context of linear systems, it often refers to subspaces of the state space that are invariant under the action of the system's dynamics when a control input is applied.

A **convex cone** is a fundamental concept in mathematics, particularly in linear algebra and convex analysis.

In the context of linear algebra and functional analysis, a **cyclic subspace** is a specific type of subspace generated by the action of a linear operator on a particular vector. Often discussed in relation to operators on Hilbert spaces or finite-dimensional vector spaces, a cyclic subspace can be defined as follows: Let \( A \) be a linear operator on a vector space \( V \), and let \( v \in V \) be a vector.

A defective matrix is a square matrix that does not have a complete set of linearly independent eigenvectors. This means that its algebraic multiplicity (the number of times an eigenvalue occurs as a root of the characteristic polynomial) is greater than its geometric multiplicity (the number of linearly independent eigenvectors associated with that eigenvalue). In other words, a matrix is considered defective if it cannot be diagonalized.

Naira Hovakimyan is a prominent figure in the field of engineering, particularly known for her work in control systems and robotics. She is a professor at the University of Illinois at Urbana-Champaign and has made significant contributions to areas such as control theory, optimization, and autonomous systems. Hovakimyan has published numerous research papers and has been recognized for her expertise in these fields.

A **definite quadratic form** refers to a specific type of quadratic expression in multiple variables that has particular properties regarding the sign of its output. In mathematical terms, a quadratic form can generally be represented as: \[ Q(\mathbf{x}) = \mathbf{x}^T A \mathbf{x} \] where: - \(\mathbf{x}\) is a vector of variables (e.g., \((x_1, x_2, ...

In linear algebra and functional analysis, the concept of a dual basis is tied to the idea of dual spaces.

Eigenplane is a technique related to the fields of machine learning and computer vision that typically involves dimensionality reduction and representation learning. It is often used to represent complex data by finding a lower-dimensional space that captures the essential features of the data while retaining its important characteristics.

Eigenvalue perturbation refers to the study of how the eigenvalues and eigenvectors of a matrix change when the matrix is slightly altered or perturbed. This concept is particularly important in linear algebra, numerical analysis, and various applied fields such as physics and engineering, where systems are often subject to small variations.

Flexural modulus, also known as bending modulus or flexural rigidity, is a measure of a material's stiffness when subjected to bending or flexural loads. It quantifies the relationship between stress (force per unit area) and strain (deformation per unit length) in a material when it is bent. The flexural modulus is typically defined in terms of the slope of the stress-strain curve during a flexural test, specifically in the elastic region of the material.

No-arbitrage bounds are a fundamental concept in financial economics and derivatives pricing that indicate ranges within which the prices of financial instruments should logically fall to prevent arbitrage opportunities. Arbitrage refers to the practice of taking advantage of price differences in different markets to earn a risk-free profit. No-arbitrage bounds establish conditions under which an asset's price must lie to ensure that no opportunities exist for arbitrage.

Optimal stopping is a decision-making problem in probability theory and statistics, where one must decide the best time to take a particular action in order to maximize an expected reward or minimize a cost. The key challenge in optimal stopping is that the decision-maker often does not know the future values of the processes involved, making it necessary to make choices based on partial information.

Profit at Risk (PaR) is a financial metric used to assess the potential risk to a company's profits from various adverse market conditions or operational factors. It is similar in concept to Value at Risk (VaR), which focuses on the potential loss in the value of an investment or portfolio over a specified time period, but PaR specifically targets the impact on profits rather than on asset values.

Stochastic volatility jump refers to a concept in financial mathematics and quantitative finance, particularly within the context of modeling asset prices and their volatility. It combines two key ideas: stochastic volatility and jumps in asset prices. 1. **Stochastic Volatility**: This concept allows for the volatility of an asset's returns to change over time and to be influenced by random factors. In traditional models, such as the Black-Scholes model, volatility is assumed to be constant.

The Taleb distribution is a family of probability distributions introduced by Nassim Nicholas Taleb, particularly in the context of modeling events that have low probability but high impact, often referred to as "black swan" events. It is not a standard distribution like the normal distribution but is instead tailored to account for phenomena in finance and other domains where extreme events occur frequently. The Taleb distribution, particularly in its applications, addresses the characteristics of skewness and kurtosis associated with such events.

Time-weighted return (TWR) is a method of measuring the performance of an investment portfolio that eliminates the impact of cash flows (deposits and withdrawals) made during the investment period. This makes it particularly useful for evaluating the performance of an investment manager, as it reflects the manager's ability to generate returns independent of the timing of cash flows. The time-weighted return is calculated by breaking down the investment period into sub-periods, typically corresponding to the dates when cash flows occur.

Realized variance is a statistical measure used to quantify the variability of asset returns over a specified period, typically applied in the context of financial markets. It is calculated by using high-frequency data, such as minute-by-minute or daily returns, to provide a more accurate estimate of the variance of an asset's returns.

In economics, "regular distribution" isn't a commonly used term like "normal distribution" or "log-normal distribution," which refer to specific statistical distributions used to model data in various contexts. However, it may refer to the general concept of "regular" in the context of how resources, income, or wealth are distributed among individuals or groups in an economy. Often, regular distribution may be sought in discussions about equity and fairness in economic systems.

Returns-based style analysis (RBSA) is a quantitative method used to evaluate the investment style and risk exposures of a portfolio, typically employed in the context of mutual funds or investment portfolios. It analyzes the historical returns of a fund to identify its underlying investment strategy and the factors that drive its performance. Key aspects of Returns-based style analysis include: 1. **Regression Analysis**: RBSA typically uses regression techniques to relate the returns of the portfolio to the returns of various benchmark indexes or factors.