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In mathematics, a fixed-point theorem is a result saying that a function F will have at least one fixed point (a point x for which F(x) = x), under some conditions on F that can be stated in general terms.[1]

In mathematical analysis

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The Banach fixed-point theorem (1922) gives a general criterion guaranteeing that, if it is satisfied, the procedure of iterating a function yields a fixed point.[2]

By contrast, the Brouwer fixed-point theorem (1911) is a non-constructive result: it says that any continuous function from the closed unit ball in n-dimensional Euclidean space to itself must have a fixed point,[3] but it doesn't describe how to find the fixed point (see also Sperner's lemma).

For example, the cosine function is continuous in [−1, 1] and maps it into [−1, 1], and thus must have a fixed point. This is clear when examining a sketched graph of the cosine function; the fixed point occurs where the cosine curve y = cos(x) intersects the line y = x. Numerically, the fixed point (known as the Dottie number) is approximately x = 0.73908513321516 (thus x = cos(x) for this value of x).

The Lefschetz fixed-point theorem[4] (and the Nielsen fixed-point theorem)[5] from algebraic topology is notable because it gives, in some sense, a way to count fixed points.

There are a number of generalisations to Banach fixed-point theorem and further; these are applied in PDE theory. See fixed-point theorems in infinite-dimensional spaces.

The collage theorem in fractal compression proves that, for many images, there exists a relatively small description of a function that, when iteratively applied to any starting image, rapidly converges on the desired image.[6]

In algebra and discrete mathematics

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The Knaster–Tarski theorem states that any order-preserving function on a complete lattice has a fixed point, and indeed a smallest fixed point.[7] See also Bourbaki–Witt theorem.

The theorem has applications in abstract interpretation, a form of static program analysis.

A common theme in lambda calculus is to find fixed points of given lambda expressions. Every lambda expression has a fixed point, and a fixed-point combinator is a "function" which takes as input a lambda expression and produces as output a fixed point of that expression.[8] An important fixed-point combinator is the Y combinator used to give recursive definitions.

In denotational semantics of programming languages, a special case of the Knaster–Tarski theorem is used to establish the semantics of recursive definitions. While the fixed-point theorem is applied to the "same" function (from a logical point of view), the development of the theory is quite different.

The same definition of recursive function can be given, in computability theory, by applying Kleene's recursion theorem.[9] These results are not equivalent theorems; the Knaster–Tarski theorem is a much stronger result than what is used in denotational semantics.[10] However, in light of the Church–Turing thesis their intuitive meaning is the same: a recursive function can be described as the least fixed point of a certain functional, mapping functions to functions.

The above technique of iterating a function to find a fixed point can also be used in set theory; the fixed-point lemma for normal functions states that any continuous strictly increasing function from ordinals to ordinals has one (and indeed many) fixed points.

Every closure operator on a poset has many fixed points; these are the "closed elements" with respect to the closure operator, and they are the main reason the closure operator was defined in the first place.

Every involution on a finite set with an odd number of elements has a fixed point; more generally, for every involution on a finite set of elements, the number of elements and the number of fixed points have the same parity. Don Zagier used these observations to give a one-sentence proof of Fermat's theorem on sums of two squares, by describing two involutions on the same set of triples of integers, one of which can easily be shown to have only one fixed point and the other of which has a fixed point for each representation of a given prime (congruent to 1 mod 4) as a sum of two squares. Since the first involution has an odd number of fixed points, so does the second, and therefore there always exists a representation of the desired form.[11]

List of fixed-point theorems

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See also

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Footnotes

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Fixed-point theorem

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In mathematics, a fixed-point theorem asserts the existence of a fixed point for a function f:X→Xf: X \to X, defined as an element x∈Xx \in X satisfying f(x)=xf(x) = x, under specific conditions such as continuity on compact sets or contractivity in complete metric spaces.[1] These theorems constitute a core area of functional analysis and topology, providing tools to prove the existence (and sometimes uniqueness) of solutions to equations across various domains.[2] Among the most influential is Brouwer's fixed-point theorem, established in 1911, which states that every continuous mapping from a closed nn-dimensional ball in Rn\mathbb{R}^n to itself admits at least one fixed point.[3] This result, proven using algebraic topology or degree theory, underpins applications in equilibrium theory, such as the existence of Nash equilibria in game theory via extensions like Kakutani's theorem.[1] Complementing it in metric spaces is the Banach fixed-point theorem (also known as the contraction mapping theorem), which guarantees a unique fixed point for any contraction— a mapping where distances are reduced by a factor k<1k < 1—on a complete metric space.[2] This theorem, dating to 1922, is pivotal for solving integral and differential equations by iterative methods and establishing unique solutions in optimization problems.[1] Fixed-point theory extends to more abstract settings, including the Schauder-Tychonoff theorem for continuous mappings on compact convex subsets of locally convex topological vector spaces, which generalizes Brouwer's result to infinite dimensions.[2] Its applications span dynamical systems, where fixed points represent equilibria; economics, for proving market equilibria; and numerical analysis, for convergence of algorithms.[1] Overall, these theorems highlight the interplay between geometric, metric, and order-theoretic structures in ensuring solution existence without explicit construction.[2]

General Concepts

Definition and Examples

In mathematics, a fixed point of a function f:X→Xf: X \to X is an element x∈Xx \in X such that f(x)=xf(x) = x.[4] This set-theoretic definition captures points invariant under the function's action, independent of any dynamical process. In contrast, fixed points also arise in iterative contexts, where a sequence defined by xn+1=f(xn)x_{n+1} = f(x_n) may converge to a limit x∗x^* satisfying f(x∗)=x∗f(x^*) = x^*, provided the iteration stabilizes.[5] Basic examples illustrate these concepts clearly. For the identity function f(x)=xf(x) = x on the real numbers, every point is a fixed point since f(x)=xf(x) = x holds for all xx.[6] A constant function f(x)=cf(x) = c for some constant c∈Xc \in X has exactly one fixed point at x=cx = c, as f(c)=cf(c) = c.[6] Graphically, fixed points of a function ff on the reals correspond to intersections of the curve y=f(x)y = f(x) with the line y=xy = x.[7] In iterative processes, fixed points represent attractors or equilibria. For instance, starting from an initial x0x_0, the sequence xn+1=f(xn)x_{n+1} = f(x_n) converges to a fixed point under suitable conditions on ff, such as contractivity, yielding solutions to equations like f(x)=xf(x) = x.[5] Not all functions possess fixed points; for example, f(x)=x+1f(x) = x + 1 on the real numbers has none, since f(x)−x=1>0f(x) - x = 1 > 0 for all xx, implying no solution to f(x)=xf(x) = x.[6] Theorems like Brouwer's guarantee existence for continuous functions on compact convex sets, such as the unit ball in Rn\mathbb{R}^n.[8]

Historical Overview

The concept of fixed points emerged in the late 19th century through Henri Poincaré's investigations into dynamical systems, where he formulated early results in 1886 treating periodic orbits as fixed points of return maps in celestial mechanics.[9] This laid intuitive groundwork for later theorems by linking fixed points to the stability and existence of periodic solutions in differential equations.[9] A pivotal advancement occurred in 1911–1912 with Luitzen E. J. Brouwer's topological contributions, proving that any continuous mapping of a closed ball in Euclidean space to itself has a fixed point, marking the birth of fixed-point theory in algebraic topology.[10] Brouwer's work, building on Poincaré's ideas, shifted focus from specific dynamical contexts to general existence guarantees in continuous functions.[10] Key developments followed in the 1920s and mid-20th century, including Stefan Banach's 1922 contraction mapping theorem, which established uniqueness and constructive proofs for fixed points in complete metric spaces under contraction conditions.[9] Alfred Tarski extended the theory in 1955 with his lattice-theoretical fixed-point theorem, demonstrating that monotone functions on complete lattices possess fixed points forming a complete sublattice, with applications to ordered structures.[11] Throughout the 20th century, fixed-point theory evolved from analytic foundations to broader algebraic and combinatorial frameworks, particularly after the 1940s, as theorems like Brouwer's and Banach's inspired extensions in topology and set theory for handling non-linear problems and infinite-dimensional spaces.[10]

Theorems in Mathematical Analysis

Brouwer's Fixed-Point Theorem

Brouwer's fixed-point theorem asserts that for any positive integer nn, every continuous function f:Dn→Dnf: D^n \to D^n, where Dn={x∈Rn:∥x∥≤1}D^n = \{ x \in \mathbb{R}^n : \|x\| \leq 1 \} is the closed unit ball in Rn\mathbb{R}^n, admits at least one fixed point x∈Dnx \in D^n satisfying f(x)=xf(x) = x. This result was established by Luitzen Egbertus Jan Brouwer in his foundational work on topology.[12] The theorem highlights the topological obstruction to "deforming" a ball continuously onto its boundary without fixed points, underscoring the rigidity of continuous maps on compact sets. The theorem extends to more general domains: any continuous self-map f:K→Kf: K \to K on a nonempty compact convex subset KK of Euclidean space Rm\mathbb{R}^m has a fixed point, since such sets are homeomorphic to the closed ball via affine transformations preserving continuity and convexity. This generalization preserves the core idea that compactness and convexity ensure the existence of points unmoved by the map, without requiring metric contractions as in related results. A key insight into the proof arises from the equivalent no-retraction theorem, which states there exists no continuous retraction from DnD^n onto its boundary ∂Dn=Sn−1\partial D^n = S^{n-1}. To see the connection, suppose ff has no fixed point; then the line segment from xx to f(x)f(x) intersects the boundary at a unique point r(x)r(x), defining a continuous retraction r:Dn→Sn−1r: D^n \to S^{n-1}. The nonexistence of such a retraction follows from Brouwer's earlier invariance of domain theorem, which implies that Rn\mathbb{R}^n minus a point is not homeomorphic to Rn\mathbb{R}^n, preventing the boundary from "filling" the ball topologically. Brouwer's original argument in 1911–1912 employed the topological degree of a map, a homotopy-invariant integer measuring how many times the map wraps the domain around the codomain, showing that the degree of the identity on DnD^n cannot retract to degree zero on the boundary. Geometrically, the theorem manifests intuitively in low dimensions. In one dimension (n=1n=1), D1=[0,1]D^1 = [0,1], and the result reduces to the intermediate value theorem applied to g(x)=f(x)−xg(x) = f(x) - x: since g(0)≥0g(0) \geq 0 and g(1)≤0g(1) \leq 0, continuity forces g(c)=0g(c) = 0 for some c∈[0,1]c \in [0,1]. In two dimensions, no continuous map can shrink the disk D2D^2 onto its boundary circle S1S^1 without "tearing," as visualized by attempting to push all interior points outward while fixing the boundary—this always leaves some interior point unmoved due to the Jordan curve theorem's implications on separating the plane. The homotopy invariance of the topological degree ensures that small perturbations of ff preserve the existence of fixed points, linking the theorem to broader stability in continuous deformations.

Banach Fixed-Point Theorem

The Banach fixed-point theorem, also known as the contraction mapping principle, asserts that if (X,d)(X, d) is a complete metric space and f:X→Xf: X \to X is a contraction mapping—meaning there exists a constant k∈[0,1)k \in [0, 1) such that d(f(x),f(y))≤k d(x,y)d(f(x), f(y)) \leq k \, d(x, y) for all x,y∈Xx, y \in X—then ff admits a unique fixed point x∗∈Xx^* \in X satisfying f(x∗)=x∗f(x^*) = x^*.[13] This result was established by the Polish mathematician Stefan Banach in his 1922 doctoral dissertation.[14] The theorem provides not only existence and uniqueness but also a constructive method to approximate the fixed point through iteration, making it a cornerstone of analysis with broad applicability.[15] To outline the proof, begin with an arbitrary initial point x0∈Xx_0 \in X and define the sequence {xn}\{x_n\} iteratively by xn+1=f(xn)x_{n+1} = f(x_n) for n≥0n \geq 0. The contraction property implies d(xn+1,xn)≤kn d(x1,x0)d(x_{n+1}, x_n) \leq k^n \, d(x_1, x_0) for all n≥1n \geq 1.[16] For m>nm > n, the triangle inequality yields
d(xm,xn)≤∑i=nm−1d(xi+1,xi)≤d(x1,x0)∑i=nm−1ki≤kn1−kd(x1,x0). d(x_m, x_n) \leq \sum_{i=n}^{m-1} d(x_{i+1}, x_i) \leq d(x_1, x_0) \sum_{i=n}^{m-1} k^i \leq \frac{k^n}{1 - k} d(x_1, x_0).
As n→∞n \to \infty, the right-hand side approaches zero independently of mm, so {xn}\{x_n\} is a Cauchy sequence. By completeness of XX, it converges to some x∗∈Xx^* \in X. Continuity of ff (which follows from the contraction condition) ensures x∗=f(x∗)x^* = f(x^*). For uniqueness, suppose y∗≠x∗y^* \neq x^* is another fixed point; then d(x∗,y∗)=d(f(x∗),f(y∗))≤k d(x∗,y∗)d(x^*, y^*) = d(f(x^*), f(y^*)) \leq k \, d(x^*, y^*), implying d(x∗,y∗)(1−k)≤0d(x^*, y^*) (1 - k) \leq 0 and thus d(x∗,y∗)=0d(x^*, y^*) = 0, a contradiction.[16] The iterative process also provides an explicit error bound: letting x∗x^* be the fixed point,
d(xn,x∗)≤∑i=n∞d(xi+1,xi)≤kn1−kd(x1,x0). d(x_n, x^*) \leq \sum_{i=n}^{\infty} d(x_{i+1}, x_i) \leq \frac{k^n}{1 - k} d(x_1, x_0).
This estimate quantifies the rate of convergence, which is linear with ratio kk.[16] A classic example is solving the equation x=cos⁡xx = \cos x on R\mathbb{R}, where the real line with the standard metric is complete. Define f(x)=cos⁡xf(x) = \cos x; then ∣f′(x)∣=∣sin⁡x∣≤1|f'(x)| = |\sin x| \leq 1, but to verify contraction, restrict to an interval like [0,1][0, 1] (also complete), where ∣f′(x)∣≤sin⁡1≈0.841<1|f'(x)| \leq \sin 1 \approx 0.841 < 1, so k=sin⁡1k = \sin 1. Starting from x0=0x_0 = 0, the Picard iterates xn+1=cos⁡xnx_{n+1} = \cos x_n converge to the unique fixed point x∗≈0.739085x^* \approx 0.739085, known as the Dottie number.[16] In the context of differential equations, the theorem underpins the Picard–Lindelöf theorem, which guarantees local existence and uniqueness of solutions to initial value problems of the form y′=f(t,y)y' = f(t, y), y(t0)=y0y(t_0) = y_0, under Lipschitz continuity in yy. By recasting the problem as a fixed-point equation in the complete metric space of continuous functions on a suitable interval, the integral operator is a contraction for small time intervals, yielding a unique solution via successive approximations.[15]

Theorems in Topology and Algebra

Tarski's Fixed-Point Theorem

Tarski's fixed-point theorem addresses the existence of fixed points for functions defined on complete lattices, providing a foundational result in order theory. Formulated by Alfred Tarski in 1955, the theorem states that if LL is a complete lattice and f:L→Lf: L \to L is a monotone function—meaning x≤yx \leq y implies f(x)≤f(y)f(x) \leq f(y) for all x,y∈Lx, y \in L—then the set of all fixed points of ff, defined as {x∈L∣f(x)=x}\{x \in L \mid f(x) = x\}, itself forms a complete lattice.[17] A complete lattice is a partially ordered set where every subset has both a supremum (least upper bound, denoted sup⁡\sup) and an infimum (greatest lower bound, denoted inf⁡\inf).[17] The theorem guarantees not only the existence of fixed points but also identifies the least and greatest ones explicitly: the least fixed point is sup⁡{x∈L∣x≤f(x)}\sup \{x \in L \mid x \leq f(x)\}, and the greatest fixed point is inf⁡{x∈L∣f(x)≤x}\inf \{x \in L \mid f(x) \leq x\}.[17] These characterizations arise from the completeness of LL, ensuring the relevant suprema and infima exist. A fixed point xx satisfies the equation x=f(x)x = f(x), and the collection of all such points inherits the lattice operations of meet (∧\wedge, infimum) and join (∨\vee, supremum) from LL.[17] The proof relies on the Knaster-Tarski construction, which proceeds by transfinite iteration starting from the bottom or top elements of the lattice, showing that the fixed points form a nonempty, complete sublattice.[17] In algebraic terms, lattices are equipped with binary meet and join operations that generalize intersection and union, with complete lattices extending this to arbitrary subsets; a classic example is the power set of a set, ordered by inclusion, where monotone functions often correspond to closure operators that add elements while preserving the order.[17] In domain theory, a refinement known as Scott continuity—requiring the function to preserve directed suprema—ensures the least fixed point exists for continuous functions on directed complete partial orders, building on Tarski's result.[18]

Sperner's Lemma

Sperner's lemma, established by Emanuel Sperner in 1928 as part of his doctoral thesis, is a fundamental combinatorial theorem that links discrete labelings of simplicial complexes to the existence of fully labeled subsimplices, serving as a discrete counterpart to Brouwer's fixed-point theorem in topology. The theorem considers an nn-dimensional simplex Δn\Delta^n that is triangulated into smaller nn-simplices. A labeling function σ\sigma assigns to each vertex of the triangulation an integer from the set {1,2,…,n+1}\{1, 2, \dots, n+1\}, subject to boundary conditions: on the face of Δn\Delta^n opposite the vertex labeled ii, no vertex receives the label ii. This proper Sperner labeling ensures that boundary faces use only the appropriate subset of labels.[19] Sperner's lemma asserts that under such a labeling, the number of small nn-simplices whose vertices bear all distinct labels 11 through n+1n+1 is odd, guaranteeing at least one such fully labeled subsimplex. This result implies Brouwer's fixed-point theorem discretely, as continuous maps on the simplex can be approximated by simplicial maps whose labelings yield fully labeled simplices corresponding to fixed points.[19] The proof employs a graph-theoretic parity argument, often illustrated in the two-dimensional case for clarity. Consider a triangulation of a 22-simplex (triangle) with vertices labeled 11, 22, and 33. The boundary edge opposite vertex 33 (spanned by vertices 11 and 22) uses only labels 11 and 22, and similarly for the other edges. Form a graph where small triangles are vertices, and edges between them correspond to shared edges labeled {1,2}\{1,2\} (termed "doors"). The outer boundary has an odd number of such doors (specifically, one more 11-22 crossing on the base than entrances). Each fully labeled small triangle has exactly one {1,2}\{1,2\}-edge (odd degree in the dual graph), while others have an even number (zero or two) of such edges (even degree). By the handshaking lemma, the number of odd-degree vertices (fully labeled triangles) must be odd to match the boundary parity.[20][19] This argument extends to higher dimensions via induction on the dimension, counting oriented simplices or using similar parity invariants on boundary facets. In the combinatorial 22D example, a simple triangulation of a triangle labeled 11-22-33 at vertices might divide it into three small triangles by connecting the centroid to the vertices. A proper labeling could assign labels such that only one small triangle receives all three labels, satisfying the odd count (here, 11). More complex triangulations, like those with dozens of small triangles, invariably yield an odd number—often 11, 33, or 55—of 123123-labeled ones, demonstrating the lemma's guarantee without exhaustive enumeration.[20] This discrete structure connects to Brouwer's continuous theorem through simplicial approximation: a continuous function f:Δn→Δnf: \Delta^n \to \Delta^n is approximated by a simplicial map on a fine triangulation, with labels derived from barycentric coordinates (label ii if the ii-th coordinate of ff is maximal), ensuring a fully labeled simplex implies a fixed point nearby.[19]

Applications and Extensions

In Economics and Game Theory

Fixed-point theorems play a central role in establishing the existence of equilibria in economic models and game theory. In non-cooperative games, John Nash demonstrated that every finite game has at least one Nash equilibrium (possibly mixed), where no player can improve their payoff by unilaterally deviating from their strategy.[21] This Nash equilibrium corresponds to a fixed point of the best-response correspondence, which maps each player's strategy profile to the set of optimal responses given others' strategies. Nash's proof applies Brouwer's fixed-point theorem to the continuous best-response map on the compact convex set of mixed strategies in the probability simplex.[21] For mixed strategies, which allow randomization, the best-response correspondence becomes set-valued, and existence relies on Kakutani's generalization of Brouwer's theorem from 1941, applicable to upper hemicontinuous correspondences on compact convex sets in finite-dimensional spaces.[22] A simple example is the two-player coordination game, such as the "battle of the sexes," where players prefer to coordinate but on different actions (e.g., one prefers opera, the other football). Pure-strategy equilibria exist at mutual coordination points, but mixed strategies may also form equilibria if players randomize to hedge uncertainty, with the fixed point ensuring stability under best responses. In general equilibrium theory, fixed-point theorems underpin the existence of Walrasian equilibria in competitive markets. The seminal Arrow-Debreu model (1954) proves that, under assumptions of continuous preferences, convex production sets, and local non-satiation, there exists a price vector and allocation where supply equals demand for all goods.[23] This equilibrium arises as a fixed point of the excess demand correspondence, which is upper hemicontinuous and maps the price simplex (normalized prices summing to one) into itself, satisfying Walras' law (aggregate excess demand is zero at equilibrium prices). Kakutani's theorem is key, handling the set-valued nature of demand correspondences due to indifference.[22] Post-2000 developments extend fixed-point methods to behavioral economics, incorporating prospect theory to model deviations from rationality, such as loss aversion and probability weighting. In games with prospect-theoretic preferences, equilibria are defined via fixed points of transformed best-response mappings that account for reference-dependent utilities and decision weights. For instance, under cumulative prospect theory, finite normal-form games admit mixed equilibria as fixed points of upper hemicontinuous correspondences, ensuring existence despite behavioral distortions.[24] This framework analyzes how prospect theory alters traditional equilibria, often leading to more risk-averse or status-quo-biased outcomes in strategic interactions.

In Computer Science

In denotational semantics for programming languages, fixed-point theorems enable the formal definition of recursive procedures by interpreting them as least fixed points of semantic functions in domain theory, a framework pioneered by Dana Scott in the 1970s to model computation in complete partial orders (cpos).[25] This approach solves recursive equations by applying the Knaster-Tarski theorem, ensuring the existence of a least fixed point that captures the minimal solution satisfying the recursion, such as the denotation of a recursive function in lambda calculus.[26] For instance, the semantics of a recursive list-processing function is constructed iteratively from approximations, converging to the fixed point that defines its input-output behavior in a domain of partial functions.[27] Fixed-point iteration methods, grounded in the Banach fixed-point theorem, are widely used in numerical computing to solve nonlinear systems by repeatedly applying a contraction mapping until convergence to a unique fixed point.[15] Newton's method exemplifies this, reformulated as finding roots of f(x)=0f(x) = 0 via the iteration xn+1=xn−f(xn)/f′(xn)x_{n+1} = x_n - f(x_n)/f'(x_n), which acts as a contraction in a suitably chosen norm around the root, guaranteeing quadratic convergence under Lipschitz conditions on the derivative.[28] These techniques are essential for practical algorithms in optimization and simulation, where the theorem ensures uniqueness and stability in complete metric spaces.[16] In logic and automata theory, the modal μ-calculus employs fixed points to express properties of transition systems, with the least fixed-point operator μ defining inductive properties like reachability and the greatest fixed-point operator ν capturing co-inductive ones like safety, enabling model checking via fixed-point computations.[29] For example, the formula νp.◊p\nu p. \Diamond p asserts the existence of an infinite path, computed as the greatest fixed point of the predicate transformer for the diamond modality.[30] This framework underpins tools like those for verifying concurrent systems, where alternating fixed-point approximations solve the model-checking problem in polynomial time for many fragments.[31] In recent machine learning applications from the 2020s, fixed-point concepts model training dynamics, such as in generative adversarial networks (GANs) where the generator-discriminator equilibrium corresponds to a fixed point of the joint optimization operator, with convergence ensured by analyzing iterations of nonexpansive mappings.[32] Similarly, policy iteration in reinforcement learning iteratively solves Bellman fixed-point equations for value functions, converging to the optimal policy under contraction assumptions on the discount factor, as analyzed through fixed-point theory for weak contractions.[33] These methods highlight fixed points' role in guaranteeing stable learning outcomes without explicit equations, focusing on asymptotic behavior in high-dimensional spaces.[34]

References

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  4. Fixed Point -- from Wolfram MathWorld
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  9. Banach Fixed Point Theorem: 100-Year History Topics
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  12. https://doi.org/10.1007/BF01456888
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  14. Sur les opérations dans les ensembles abstraits et leur application ...
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  20. [PDF] Sperner's Lemma and Fair Division Problems - MIT OpenCourseWare
  21. [PDF] Domain Theory: An Introduction - Rice University
  22. [PDF] Chapter 10 DOMAIN THEORY AND FIXED-POINT SEMANTICS
  23. [PDF] Domain Theory
  24. [PDF] The Magic of Iteration - Penn Math
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  26. [PDF] Model Checking and the Mu-calculus
  27. [PDF] The mu-calculus and model-checking - LaBRI
  28. Convergence Rate Analysis for Fixed-Point Iterations of Generalized ...
  29. Fixed Point Theory Analysis of a Lambda Policy Iteration with ... - arXiv
  30. Convergence Analysis of Reinforcement Learning Algorithms Using ...
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