# What’s the hardest math?

Math comes in many forms and difficulties. Some types of math are considered more challenging than others, depending on the concepts involved and the level of abstraction. When looking at the broad landscape of mathematics, a few areas stand out as exceptionally complex and demanding, even for those with advanced mathematical abilities.

## Abstract Algebra

Abstract algebra, sometimes called modern algebra, is considered by many to be one of the most difficult branches of mathematics. It involves the study of algebraic structures such as groups, rings, and fields. These structures have operations that follow certain rules and exhibit properties that abstract algebra aims to understand. Unlike elementary algebra which focuses on numerical formulas and equations, abstract algebra uses more theoretical constructs and proofs to analyze abstract algebraic systems.

Some of the challenging aspects of abstract algebra include:

• High level of abstraction – Moving beyond concrete numbers and figures into working with general algebraic structures requires reasoning at a more conceptual level.
• Proofs – Proving theorems involving the properties and behaviors of algebraic structures often involves complex logical reasoning and mathematics.
• Unfamiliar concepts – Groups, rings, fields, and other abstract constructs can be difficult to grasp at first.
• Visualization – With its high level of abstraction, abstract algebra can be difficult to visualize graphically compared to other math fields.

For these reasons, abstract algebra is considered inaccessible for many non-math majors and requires a high degree of mathematical maturity even for undergraduate math students.

## Real Analysis

Real analysis focuses on the theoretical underpinnings of calculus using the concepts of real numbers and functions. It builds upon and expands introductory calculus by rigorously proving calculus theorems and properties of continuity, sequences, series, differentiation, and integration. Real analysis also involves topics like measure theory and integration over abstract spaces.

The challenges of real analysis include:

• Rigor – Real analysis relies on formal proofs and precise logic when examining functions and limits, more so than basic calculus courses.
• Abstract thinking – Moving from computational problems to proofs requires high levels of abstract reasoning.
• Subtle concepts – Ideas like convergence, continuity, and completeness are subtle and mathematically involved.
• Unintuitive results – Some conclusions of real analysis contradict intuitive understandings from basic calculus.

These difficulties make real analysis a struggle for many students without sufficient mathematical development. Having a solid foundation in writing proofs is necessary to grasp this advanced theoretical calculus.

## Complex Analysis

Complex analysis extends calculus into the complex number system, involving functions and derivatives of complex variables. This field includes complex integrals, residues, conformal mappings, and other advanced concepts. Complex numbers themselves can be challenging to understand, let alone manipulating them with calculus operations and geometric interpretations.

Reasons complex analysis can be so tricky include:

• Imaginary numbers – The foundational numbers used are imaginary, which don’t have concrete analogs.
• Multi-dimensional – Graphs use the complex plane instead of the regular 1D number line.
• Subtle behaviors – Functions of complex variables exhibit subtleties like being differentiable but not conformable.
• Obscure applications – Applications in fields like fluid dynamics and electromagnetism are highly technical.

Without solid intuition for complex numbers, their geometrical representations, and knowledge of advanced calculus, complex analysis can be impenetrable.

## Partial Differential Equations

Partial differential equations (PDEs) involve multivariable functions and their partial derivatives. These equations arise frequently when solving problems in physics, engineering, and other technical fields. However, analytically solving PDEs is notoriously difficult except for in specialized cases. There are entire fields like elliptic PDEs, parabolic PDEs, and nonlinear PDEs focusing on different types of these equations.

Reasons PDEs are considered so challenging include:

• Multivariate – Having multiple independent variables makes PDEs much harder than ODEs with one variable.
• Broad applications – PDEs model many phenomena in science but with complicated solutions.
• Analytical methods – Special techniques like separation of variables, transforms, and numerical methods are required.
• Qualitative analysis – Understanding general solution behaviors and properties is also demanding.

The complexity of deriving analytical solutions and studying qualitative properties of PDEs requires sophisticated mathematical skills.

## Algebraic Topology

Algebraic topology is a field that combines abstract algebra with topology, the study of geometric properties preserved under continuous transformations. It builds on introductory point-set topology by associating algebraic constructs like groups to topological spaces.

Some reasons algebraic topology is so challenging include:

• High abstraction – It involves very generalized topological spaces and algebraic structures.
• Obscure applications – Applications are found mainly in other advanced mathematics like string theory.
• Intricate constructs – Spaces, homology groups, and cohomology have intricate definitions.
• Multi-disciplinary – Combining algebra, geometry, and topology requires mastering each area.

The immense level of abstraction in algebraic topology makes it inaccessible for anyone without expertise in both advanced algebra and topology.

## Set Theory

Set theory is a branch of logic that studies collections of abstract objects called sets. It is considered a foundational area that other mathematics builds upon. Despite seeming straightforward on the surface, set theory includes some very complex concepts and paradoxes related to infinity, certainty, and the nature of sets themselves.

Reasons set theory is so tricky include:

• Self-referential logic – Sets containing themselves and other strange constructions lead to paradoxes.