Convex To The Origin

The Enigmatic Embrace of Convexity: A Journey to the Origin

Imagine a vast, star-studded expanse. Each star represents a point in a mathematical space. Now, imagine drawing a line connecting any two of these stars. If that line, in its entirety, remains within the constellation of stars, we’re dealing with a fascinating concept: convexity. But what if we add a crucial element – the origin, the central point (0,0) in our mathematical universe? This introduces us to the captivating world of sets that are "convex to the origin," a concept with deeper implications than you might initially suspect.

1. Defining Convexity to the Origin

In simple terms, a set of points is convex to the origin if, for any two points within the set, the line segment connecting them also lies entirely within the set and passes through the origin. Let's break this down:

Convexity: A set is convex if, for any two points within the set, the line segment connecting them is also entirely contained within the set. Think of a circle or an ellipse; no matter which two points you choose, the line connecting them stays within the shape. A star shape, on the other hand, is not convex.

To the Origin: This adds the crucial condition that the line segment must pass through the origin (0,0). This dramatically restricts the types of sets that qualify. A simple circle centred anywhere other than the origin is not convex to the origin.

Consider a simple example: the positive quadrant of a Cartesian plane (where both x and y coordinates are positive). This region is convex to the origin. Pick any two points within this quadrant; the line joining them will always stay within the quadrant and pass through (0,0). However, a circle centered at (1,1) with a radius of 1 is convex but not convex to the origin.

2. Mathematical Representation

Mathematically, a set S is convex to the origin if, for any two points x and y in S, and for any scalar λ between 0 and 1, the point λx + (1-λ)y is also in S. The crucial part is that this condition must hold for all such λ, meaning the entire line segment connecting x and y, including the point where λ = 0 (point y) and λ = 1 (point x), resides within the set and passes through the origin.

This mathematical definition allows for precise analysis and is used extensively in various fields.

3. Applications in Real-World Scenarios

The concept of convexity to the origin, while seemingly abstract, finds practical applications in several domains:

Economics: In production economics, the production possibility frontier (PPF) often exhibits convexity to the origin. This reflects the principle of increasing opportunity cost: as you produce more of one good, you must sacrifice increasingly more of another. The origin signifies no production of either good.

Operations Research: Linear programming, a cornerstone of operations research, heavily relies on the concept of convex sets. Many optimization problems involve finding the optimal solution within a convex region, often related to resource allocation and production planning. Convexity to the origin might represent feasible solutions with resource constraints centred at the origin.

Machine Learning: Convex functions play a vital role in machine learning algorithms. The optimization of many machine learning models involves minimizing a convex cost function. The origin may represent a baseline or a zero-error state.

Image Processing: In image processing, convex hulls are used for shape analysis and object recognition. Convexity to the origin might be relevant when analyzing shapes centered around a specific point of interest.

4. Beyond the Basics: Cones

A special class of sets convex to the origin are cones. A cone is a set that, if it contains a point x, it also contains λx for all λ ≥ 0. In simpler terms, if a point is in the cone, any positive scalar multiple of that point is also in the cone. This means cones always extend infinitely from the origin. Many optimization problems in engineering and economics are formulated using cones, often representing feasible regions.

5. Expanding the Horizon: Non-Convex Sets

It's crucial to understand that not all sets are convex to the origin. Many real-world scenarios involve non-convex sets, making their analysis more complex. However, techniques exist to approximate non-convex sets with convex ones, allowing for easier analysis and optimization.

Reflective Summary

The concept of "convex to the origin" is a fundamental idea in mathematics with surprisingly broad applications across various fields. It's a powerful tool for analyzing sets, understanding constraints, and solving optimization problems. The addition of the "to the origin" condition introduces a crucial constraint, dramatically narrowing the scope of eligible sets and highlighting the importance of the central point in many mathematical and real-world models. From production possibility frontiers in economics to cost functions in machine learning, its relevance spans numerous disciplines. Understanding convexity to the origin empowers us to model and solve complex problems more effectively.

FAQs

1. Q: Can a set be convex but not convex to the origin? A: Yes, absolutely. A circle not centered at the origin is a perfect example.

2. Q: What if the origin is not at (0,0)? A: The definition needs to be adjusted. The origin becomes a specified point, and the line segment connecting any two points in the set must pass through that specific point.

3. Q: Are all cones convex to the origin? A: Yes, all cones are convex to the origin by definition.

4. Q: Why is the concept of "to the origin" important? A: It introduces a critical constraint that significantly influences the type of sets being considered, making the analysis more targeted and relevant to specific problems.

5. Q: How do I determine if a given set is convex to the origin? A: Use the mathematical definition; verify that for any two points in the set, the line segment connecting them remains entirely within the set and passes through the origin. You can also visualize it graphically.

Search Results:

microeconomics - Convex to origin - precise definition 25 Mar 2024 · The term "convex to the origin" seems to have been used in the prosaic meaning of "bending towards the origin" without a formal mathematical definition. The note. Kozlik, Adolf. "Note on the Terminology Convex and Concave." The American Economic Review 31.1 (1941): 103-105. discusses the variation of early usage, which was not uniform.

microeconomics - How to determine convexity or concavity of an ... 23 Jan 2023 · By one definition, an IC is convex if the function y(x) is a convex function—this is the definition used in Nicolas Torres' answer. If that's your definition then his answer is correct. But the second definition (an IC is convex if its upper contour set is convex) matches the conditions you were given by the instructor, so is probably the one s/he had in mind. $\endgroup$

Why an indifference curve is convex to the origin? - LearnPick 27 Nov 2015 · Indifference curves are convex to the origin because the marginal utility of each product consumed decreases with subsequent consumption. This convex relationship is based upon an idea dubbed the marginal rate of substitution, which is represented by the formula (Z = change in X / change in Y).

"Convex to origin" indifference curves - Economics Stack Exchange 13 Mar 2024 · The expression tries to convey a visual notion of convexity. It is "convex to the origin" in the sense that if we "stand" at the origin, the point $(0,0)$, and "look towards" the graph, we will perceive it as convex. In contrast, if we stand "above" such a graph looking towards it, taking "our" position as being the origin, it will be concave.

Why is the indifference curve convex to origin? - Toppr The indifference Curve is convex to origin because of decreasing MRS (Marginal rate of substitution). T he curve represents the marginal rate of substitution. Hence, option (a) is correct.

Why is an indifference curve convex to origin? - Toppr An indifference curve is convex to the origin because of diminishing MRS. MRS declines continuously because of the law of diminishing marginal utility. As seen in Table, when the consumer consumes more and more of apples, his marginal utility from apples keeps on declining and he is willing to give up less and less of bananas for each apple.

Write true or false with a reason:If IC is convex to the origin, MRS ... If IC is convex to the origin, MRS should be diminishing. Because, the consumer is willing to sacrifice less and less amount of Good-Y for every additional unit of Good-X.

When is the PPF convex to the origin? - Economics Stack Exchange Given a 2X2 model (2 goods, 2 inputs), if the factor intensities (capital/labour ratio) of the two goods along the Pareto set are unequal, then we get a concave PPF. Can we get a convex PPF in some...

Why are demand and supply curves shown as concave up? 3 Jan 2020 · $\begingroup$ @TejasSubramaniam but as I said in my answer 1. Marginal cost is increasing 2. It is though of increasing at increasing rate (second half of paragraph 3).

A convex to the origin PPC implies - Toppr The slope of production possibility curve is the marginal opportunity cost which refers to the additional sacrifice that an economy must make when it shifts resources and technology from production of one commodity to the other. Therefore, if marginal opportunity cost decreases then PPC will be convex to the origin owing to decreasing slope.