Algorithm Design Principles: How to Build Procedures That Work

Last Updated June 17, 2026

Algorithm design principles explain how to create procedures that are correct, efficient, interpretable, robust, and appropriate for the problem context. An algorithm is not merely a clever trick or a sequence of instructions. It is a structured way of transforming inputs into outputs under constraints.

Good algorithm design begins before code. It asks what problem is being solved, what counts as a valid input, what counts as a correct output, what assumptions are being made, what resources are limited, what errors are possible, and what consequences follow from the procedure’s use.

A poorly designed algorithm can be fast but wrong, correct but unusable, elegant but fragile, or efficient in theory while inappropriate in context. A well-designed algorithm balances correctness, clarity, efficiency, robustness, maintainability, testability, and responsibility.

This article introduces algorithm design principles as the foundation for procedural strategy, computational judgment, and responsible technical systems.

This article explains algorithm design as disciplined procedural judgment. It introduces problem formulation, inputs, outputs, constraints, assumptions, correctness, invariants, termination, decomposition, representation, data structures, control flow, complexity, trade-offs, edge cases, robustness, interpretability, testing, verification, and responsible use. It emphasizes that algorithm design is not only about finding a working procedure. It is about choosing a procedure that fits the structure, evidence, limits, risks, and purpose of the problem.

Why Algorithm Design Principles Matter

Algorithm design principles matter because a procedure can be syntactically valid and still be conceptually weak. It may solve the wrong problem, fail on boundary cases, depend on hidden assumptions, scale poorly, produce uninterpretable results, or behave irresponsibly in real-world use.

Design principles help convert a vague goal into a disciplined procedure. They force the designer to ask not only “Can this be computed?” but also “Should it be computed this way?”

An algorithm is not only judged by whether it runs. It is judged by what it assumes, what it guarantees, what it costs, and what happens when conditions change.

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What Algorithm Design Is

Algorithm design is the practice of creating procedures for solving well-defined computational problems. It includes selecting representations, organizing steps, choosing data structures, proving correctness, estimating resource use, testing edge cases, and evaluating trade-offs.

Algorithm design is both technical and interpretive. It turns a problem description into a formal procedure, but that formal procedure depends on modeling choices.

Algorithm design is therefore a method for connecting problem structure to procedural strategy.

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Problem Formulation Before Procedure

Good algorithm design begins with problem formulation. A designer must define the task before choosing a method. Without a clear problem statement, efficiency and elegance can become distractions.

Problem formulation includes defining the input space, output space, constraints, objective, assumptions, and evaluation criteria.

Poor formulation produces poor procedure. The first algorithmic decision is deciding what problem exists.

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Inputs, Outputs, Constraints, and Assumptions

Every algorithm is defined relative to inputs, outputs, constraints, and assumptions. These should be explicit. If they remain hidden, users may apply the algorithm where its guarantees no longer hold.

A sorting algorithm assumes comparable elements. A shortest-path algorithm may assume nonnegative edge weights. A recommendation algorithm assumes that prior behavior is relevant evidence. A simulation assumes that simplified dynamics are acceptable for the question being asked.

An algorithm without visible assumptions is difficult to trust. Assumptions are part of the design, not an appendix to it.

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Correctness Before Cleverness

A clever algorithm that produces wrong results is not a good algorithm. Correctness must come before optimization, novelty, and elegance. Correctness means that for every valid input, the algorithm produces an output that satisfies the specification.

Correctness can be argued with examples, tests, invariants, induction, contradiction, exchange arguments, loop reasoning, or formal proof.

Optimization should improve a correct design, not conceal an uncertain one.

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Invariants, Termination, and Edge Cases

Invariants and termination are central to algorithmic reasoning. An invariant is a property that remains true throughout execution. Termination is the guarantee that a procedure eventually stops. Edge cases are inputs at limits where assumptions often break.

A designer should ask what must always remain true, what makes progress measurable, and what unusual cases could violate the design.

Algorithms fail when their ordinary cases are understood but their boundaries are neglected.

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Representation and Data Structure Choice

Algorithm design depends on representation. The same problem can be easy or hard depending on how information is organized. Data structures do not merely store information. They shape what operations are cheap, natural, and reliable.

A graph represented as an adjacency matrix supports different operations than a graph represented as an adjacency list. A queue supports different reasoning than a stack. A hash table supports fast lookup but depends on hashing assumptions. A tree supports hierarchy and search but requires balancing or structural discipline in some contexts.

Representation is an algorithmic decision. The wrong structure can make a simple procedure difficult; the right structure can make the intended procedure almost obvious.

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Decomposition and Composability

Decomposition breaks a problem into smaller parts. Composability allows those parts to be recombined reliably. Good decomposition reduces complexity without losing the relationships that matter.

A decomposed algorithm should have clear subproblems, clear interfaces between steps, clear assumptions for each part, and clear evidence that the assembled procedure still solves the original problem.

Decomposition is not fragmentation. It is the disciplined creation of parts that support reasoning.

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Efficiency and Complexity

Efficiency matters because algorithms operate under resource constraints. Time, memory, communication, energy, latency, and human attention may all be limited. Complexity analysis estimates how resource use grows as input size grows.

However, efficiency must be understood in context. A theoretically efficient algorithm may be difficult to implement, hard to audit, fragile under real data, or unnecessary for small inputs. A simple algorithm may be acceptable if input sizes are small and interpretability matters more than speed.

Efficiency is a design principle, not the only principle. The best algorithm is often the one whose cost profile matches the problem’s actual constraints.

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Robustness and Failure Behavior

Robust algorithms handle variation, invalid inputs, edge cases, and changing conditions without producing misleading results. Robustness does not mean an algorithm never fails. It means failure is anticipated, bounded, explicit, and recoverable where possible.

Robustness is especially important when algorithms operate in data pipelines, public platforms, scientific workflows, infrastructure systems, and AI-assisted decision environments.

A robust algorithm does not pretend the world is clean. It defines what it can handle and what it cannot.

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Interpretability and Maintainability

Algorithm design is not finished when a procedure works. Future people must be able to understand, maintain, test, adapt, and govern it. Interpretability and maintainability matter because algorithms often outlive their first implementation.

Readable structure, meaningful names, clear invariants, documented assumptions, modular boundaries, tests, and examples all support long-term reliability.

A design that cannot be explained is difficult to trust. A design that cannot be maintained is difficult to use responsibly.

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Algorithmic Strategy Patterns

Algorithm design often uses recognizable strategy patterns. These are not recipes to apply mechanically. They are families of reasoning that match different problem structures.

Choosing a strategy means asking what structure the problem has: sequence, hierarchy, recurrence, search space, optimal substructure, graph connectivity, local choice, randomness, approximation, or adaptation.

The strategy should follow the problem’s structure. A design principle is useful when it helps distinguish where a strategy fits and where it fails.

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Appropriateness and Context

A technically correct algorithm may still be inappropriate. It may optimize the wrong objective, use unavailable data, create unacceptable latency, produce outputs that users cannot interpret, or encode assumptions that are unsuitable for the institutional setting.

Appropriateness connects algorithm design to context, purpose, values, and consequences.

The right algorithm is not always the fastest or most mathematically elegant. It is the procedure that fits the problem, constraints, evidence, and consequences.

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Testing, Verification, and Evidence

Algorithm design should produce evidence. Tests show behavior on selected cases. Verification argues that stated properties hold. Complexity analysis estimates resource cost. Runtime monitoring shows behavior under real conditions. Documentation records assumptions and limitations.

Evidence makes algorithmic claims reviewable.

Algorithm design should not end with a claim that the method works. It should include the evidence by which that claim can be evaluated.

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Governance and Responsible Design

Algorithm design becomes a governance issue when procedures affect people, institutions, research claims, infrastructure, resources, or public knowledge. Governance asks who defines the objective, who reviews assumptions, who monitors behavior, who can change the procedure, and who is accountable for consequences.

Responsible design makes algorithmic choices visible enough to be questioned.

Governance does not replace algorithm design. It extends design into accountability.

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Representation Risk

Algorithm design carries representation risk because the formal problem may not match the real problem. Inputs may be proxies. Objectives may simplify values. Constraints may omit important realities. Outputs may appear more authoritative than the assumptions justify.

A procedure can be correct relative to a formal specification while still misleading in context.

Algorithm design should make the gap between formal representation and real-world meaning visible.

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Mathematics, Computation, and Modeling

A computational problem can be represented as a relation between valid inputs and acceptable outputs:

An algorithm is a procedure that maps inputs to outputs:

Correctness means the algorithm’s output satisfies the problem relation:

Cost can be described as a function of input size:

A design-quality score can be summarized as:

These models show why algorithm design is a judgment discipline. The procedure, problem, representation, cost, and context must be evaluated together.

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Python Workflow: Algorithm Design Audit

The Python workflow below creates a dependency-light audit for algorithm design principles. It scores problem formulation, input-output clarity, correctness rationale, termination argument, complexity analysis, data-structure fit, edge-case coverage, robustness, interpretability, and governance readiness.

# algorithm_design_audit.py
# Dependency-light workflow for auditing algorithm design principles.

from __future__ import annotations

from dataclasses import asdict, dataclass
from pathlib import Path
import csv
import json
from statistics import mean

ARTICLE_ROOT = Path(__file__).resolve().parents[1]
TABLES = ARTICLE_ROOT / "outputs" / "tables"
JSON_DIR = ARTICLE_ROOT / "outputs" / "json"


@dataclass(frozen=True)
class AlgorithmDesignCase:
    case_name: str
    problem_context: str
    design_choice: str
    problem_formulation: float
    input_output_clarity: float
    correctness_rationale: float
    termination_argument: float
    complexity_analysis: float
    data_structure_fit: float
    edge_case_coverage: float
    robustness: float
    interpretability: float
    governance_readiness: float


def clamp(value: float, low: float = 0.0, high: float = 100.0) -> float:
    return max(low, min(high, value))


def design_quality(case: AlgorithmDesignCase) -> float:
    return clamp(
        100.0 * (
            0.12 * case.problem_formulation
            + 0.10 * case.input_output_clarity
            + 0.12 * case.correctness_rationale
            + 0.10 * case.termination_argument
            + 0.10 * case.complexity_analysis
            + 0.10 * case.data_structure_fit
            + 0.10 * case.edge_case_coverage
            + 0.10 * case.robustness
            + 0.08 * case.interpretability
            + 0.08 * case.governance_readiness
        )
    )


def design_risk(case: AlgorithmDesignCase) -> float:
    weak_points = [
        1.0 - case.problem_formulation,
        1.0 - case.input_output_clarity,
        1.0 - case.correctness_rationale,
        1.0 - case.termination_argument,
        1.0 - case.complexity_analysis,
        1.0 - case.edge_case_coverage,
        1.0 - case.robustness,
        1.0 - case.governance_readiness,
    ]
    return clamp(100.0 * mean(weak_points))


def diagnose(quality: float, risk: float) -> str:
    if quality >= 84 and risk <= 20:
        return "strong algorithm design discipline"
    if quality >= 70 and risk <= 35:
        return "usable algorithm design with review needs"
    if risk >= 55:
        return "high design risk; formulation, correctness, termination, complexity, or robustness gaps may be present"
    return "partial design discipline; strengthen specification, proof, edge cases, and governance"


def build_cases() -> list[AlgorithmDesignCase]:
    return [
        AlgorithmDesignCase(
            case_name="Shortest-path procedure",
            problem_context="A routing workflow needs the minimum-cost path through a nonnegative weighted graph.",
            design_choice="Use Dijkstra-style priority queue strategy with explicit nonnegative-weight precondition.",
            problem_formulation=0.92,
            input_output_clarity=0.90,
            correctness_rationale=0.90,
            termination_argument=0.88,
            complexity_analysis=0.88,
            data_structure_fit=0.90,
            edge_case_coverage=0.84,
            robustness=0.82,
            interpretability=0.86,
            governance_readiness=0.84,
        ),
        AlgorithmDesignCase(
            case_name="Data validation pipeline",
            problem_context="A workflow must reject malformed records before scoring and reporting.",
            design_choice="Use schema validation, explicit errors, audit records, and edge-case tests.",
            problem_formulation=0.88,
            input_output_clarity=0.92,
            correctness_rationale=0.84,
            termination_argument=0.90,
            complexity_analysis=0.80,
            data_structure_fit=0.86,
            edge_case_coverage=0.90,
            robustness=0.88,
            interpretability=0.90,
            governance_readiness=0.90,
        ),
        AlgorithmDesignCase(
            case_name="Approximate recommendation ranking",
            problem_context="A large content system must rank many items under latency and diversity constraints.",
            design_choice="Use indexed retrieval, scoring heuristics, diversity constraints, monitoring, and human-review feedback.",
            problem_formulation=0.84,
            input_output_clarity=0.82,
            correctness_rationale=0.72,
            termination_argument=0.86,
            complexity_analysis=0.86,
            data_structure_fit=0.88,
            edge_case_coverage=0.78,
            robustness=0.80,
            interpretability=0.76,
            governance_readiness=0.86,
        ),
        AlgorithmDesignCase(
            case_name="Unspecified optimization script",
            problem_context="A script searches for a best option without defining objective, constraints, or error behavior.",
            design_choice="Ad hoc scoring and manual inspection.",
            problem_formulation=0.42,
            input_output_clarity=0.44,
            correctness_rationale=0.32,
            termination_argument=0.48,
            complexity_analysis=0.36,
            data_structure_fit=0.44,
            edge_case_coverage=0.34,
            robustness=0.36,
            interpretability=0.46,
            governance_readiness=0.30,
        ),
    ]


def run_audit() -> list[dict[str, object]]:
    rows: list[dict[str, object]] = []
    for case in build_cases():
        quality = design_quality(case)
        risk = design_risk(case)
        rows.append({
            **asdict(case),
            "design_quality": round(quality, 3),
            "design_risk": round(risk, 3),
            "diagnostic": diagnose(quality, risk),
        })
    return rows


def write_csv(path: Path, rows: list[dict[str, object]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys()))
        writer.writeheader()
        writer.writerows(rows)


def write_json(path: Path, payload: object) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")


def summarize(rows: list[dict[str, object]]) -> dict[str, object]:
    return {
        "case_count": len(rows),
        "average_design_quality": round(mean(float(row["design_quality"]) for row in rows), 3),
        "average_design_risk": round(mean(float(row["design_risk"]) for row in rows), 3),
        "highest_quality_case": max(rows, key=lambda row: float(row["design_quality"]))["case_name"],
        "highest_risk_case": max(rows, key=lambda row: float(row["design_risk"]))["case_name"],
        "interpretation": "Algorithm design quality depends on problem formulation, input-output clarity, correctness, termination, complexity, data structure fit, edge cases, robustness, interpretability, and governance."
    }


def main() -> None:
    rows = run_audit()
    summary = summarize(rows)

    write_csv(TABLES / "algorithm_design_audit.csv", rows)
    write_csv(TABLES / "algorithm_design_audit_summary.csv", [summary])
    write_json(JSON_DIR / "algorithm_design_audit.json", rows)
    write_json(JSON_DIR / "algorithm_design_audit_summary.json", summary)

    print("Algorithm design audit complete.")
    print(TABLES / "algorithm_design_audit.csv")


if __name__ == "__main__":
    main()

This workflow treats algorithm design as an auditable set of decisions rather than a black-box procedure.

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R Workflow: Design Principle Summary

The R workflow reads the Python-generated audit table and creates summary outputs and visualizations using base R. It compares design quality and design risk across synthetic algorithm-design cases.

# algorithm_design_summary.R
# Base R workflow for summarizing algorithm design principle evidence.

args <- commandArgs(trailingOnly = FALSE)
file_arg <- grep("^--file=", args, value = TRUE)

if (length(file_arg) > 0) {
  script_path <- normalizePath(sub("^--file=", "", file_arg[1]), mustWork = TRUE)
  article_root <- normalizePath(file.path(dirname(script_path), ".."), mustWork = TRUE)
} else {
  article_root <- getwd()
}

setwd(article_root)

tables_dir <- file.path(article_root, "outputs", "tables")
figures_dir <- file.path(article_root, "outputs", "figures")

if (!dir.exists(tables_dir)) {
  dir.create(tables_dir, recursive = TRUE)
}

if (!dir.exists(figures_dir)) {
  dir.create(figures_dir, recursive = TRUE)
}

input_path <- file.path(tables_dir, "algorithm_design_audit.csv")

if (!file.exists(input_path)) {
  stop(paste("Missing", input_path, "Run the Python workflow first."))
}

data <- read.csv(input_path, stringsAsFactors = FALSE)

summary_table <- data.frame(
  case_count = nrow(data),
  average_design_quality = mean(data$design_quality),
  average_design_risk = mean(data$design_risk),
  highest_quality_case = data$case_name[which.max(data$design_quality)],
  highest_risk_case = data$case_name[which.max(data$design_risk)]
)

write.csv(
  summary_table,
  file.path(tables_dir, "r_algorithm_design_summary.csv"),
  row.names = FALSE
)

comparison_matrix <- rbind(
  data$design_quality,
  data$design_risk
)

colnames(comparison_matrix) <- data$case_name
rownames(comparison_matrix) <- c("Design quality", "Design risk")

png(
  file.path(figures_dir, "algorithm_design_quality_vs_risk.png"),
  width = 1400,
  height = 800
)

barplot(
  comparison_matrix,
  beside = TRUE,
  las = 2,
  ylim = c(0, 100),
  ylab = "Score",
  main = "Algorithm Design Quality vs. Design Risk"
)

legend(
  "topleft",
  legend = rownames(comparison_matrix),
  pch = 15,
  bty = "n"
)

grid()
dev.off()

png(
  file.path(figures_dir, "algorithm_design_dimensions.png"),
  width = 1400,
  height = 800
)

dimension_means <- colMeans(data[, c(
  "problem_formulation",
  "input_output_clarity",
  "correctness_rationale",
  "termination_argument",
  "complexity_analysis",
  "data_structure_fit",
  "edge_case_coverage",
  "robustness",
  "interpretability",
  "governance_readiness"
)]) * 100

barplot(
  dimension_means,
  las = 2,
  ylim = c(0, 100),
  ylab = "Average score",
  main = "Average Algorithm Design Evidence by Dimension"
)

grid()
dev.off()

print(summary_table)

This workflow helps compare shortest-path design, data validation, recommendation ranking, and under-specified optimization by problem clarity, correctness, complexity, robustness, interpretability, and governance.

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GitHub Repository

The companion repository for this article will provide reproducible code, synthetic datasets, workflow documentation, generated outputs, design-principle audits, calculator scripts, test examples, and governance artifacts that extend the article into executable examples.

articles/algorithm-design-principles/
├── python/
│   ├── algorithm_design_audit.py
│   ├── correctness_examples.py
│   ├── invariant_examples.py
│   ├── complexity_examples.py
│   ├── edge_case_examples.py
│   ├── strategy_pattern_examples.py
│   ├── calculators/
│   │   ├── design_quality_calculator.py
│   │   └── complexity_tradeoff_calculator.py
│   └── tests/
├── r/
│   ├── algorithm_design_summary.R
│   ├── design_principle_visualization.R
│   └── design_governance_report.R
├── julia/
│   ├── algorithm_design_examples.jl
│   └── complexity_tradeoff_examples.jl
├── sql/
│   ├── schema_algorithm_design_cases.sql
│   ├── schema_design_principles.sql
│   └── algorithm_design_queries.sql
├── haskell/
│   ├── AlgorithmDesign.hs
│   ├── CorrectnessInvariant.hs
│   └── Main.hs
├── rust/
│   └── src/
├── go/
│   └── main.go
├── c/
│   └── algorithm_design_audit.c
├── cpp/
│   └── algorithm_design_audit.cpp
├── fortran/
│   └── design_quality_model.f90
├── java/
│   └── src/main/java/org/contentcatalyst/algorithms/
├── typescript/
│   └── src/
├── prolog/
│   └── algorithm_design_rules.pl
├── racket/
│   └── algorithm_design_checker.rkt
├── docs/
│   ├── methodology.md
│   ├── article-notes.md
│   ├── algorithm-design-principles.md
│   ├── governance-notes.md
│   └── responsible-use.md
├── data/
│   └── synthetic_algorithm_design_cases.csv
├── outputs/
│   ├── tables/
│   ├── figures/
│   ├── json/
│   ├── logs/
│   └── reports/
├── notebooks/
│   └── algorithm_design_principles_walkthrough.ipynb
├── canvas/
│   ├── canvas_manifest.json
│   ├── canvas_cards.json
│   └── canvas_index.md
└── shared/
    ├── schemas/
    ├── templates/
    ├── taxonomies/
    ├── benchmarks/
    └── governance/

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A Practical Method for Reviewing Algorithm Design

A practical algorithm-design review begins with the question: what evidence shows that this procedure fits the problem, works correctly, uses resources appropriately, and remains responsible in context?

Algorithm review should happen before a method becomes infrastructure.

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Common Pitfalls

A common pitfall is treating algorithm design as a race to implement. The faster path to code may skip the harder questions: what is the problem, what counts as correct, what assumptions are required, what happens at boundaries, and what evidence supports the result?

Common pitfalls include:

• unclear problem statement: the algorithm solves a vague or shifting task;
• hidden assumptions: guarantees depend on conditions users do not know;
• correctness skipped: tests exist but no rationale explains why the method works;
• termination risk: loops or recursion lack clear progress conditions;
• wrong data structure: representation makes common operations expensive or fragile;
• premature optimization: speed is improved before correctness and clarity are established;
• edge-case neglect: empty, extreme, duplicate, missing, or invalid inputs are ignored;
• context mismatch: a technically valid method is inappropriate for the actual use case;
• opaque output: users cannot interpret what the algorithm returns;
• missing governance: assumptions, changes, failures, and review decisions are not recorded.

The remedy is to treat algorithm design as a sequence of accountable choices.

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Why Algorithm Design Shapes Computational Judgment

Algorithm design principles matter because procedures shape what computation can do, what it cannot do, and how people understand its results. A good algorithm is not merely one that runs. It is one whose problem is well defined, whose inputs and outputs are clear, whose assumptions are visible, whose correctness can be argued, whose termination is understood, whose cost is appropriate, whose edge cases are tested, and whose use fits the context.

Algorithm design is therefore a form of computational judgment. It requires choosing representations, strategies, data structures, stopping conditions, tests, and trade-offs. It requires knowing when exactness matters and when approximation is acceptable. It requires balancing efficiency with clarity, robustness with simplicity, and automation with accountability.

The principles introduced here prepare the way for the procedural strategies that follow: iteration, recursion, search, sorting, divide-and-conquer, greedy algorithms, dynamic programming, exhaustive search, randomized methods, approximation, heuristics, and adaptive search. Each strategy is powerful only when used with design discipline.

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Related Articles

• Testing, Verification, and Computational Reliability
• Iteration, Recursion, and Control Flow
• Decomposition and Stepwise Reasoning
• Problems, Procedures, and Formalization
• Termination, Invariants, and Edge Cases
• Search and Sorting as Foundational Algorithms
• Divide-and-Conquer Methods
• Computational Complexity and Scalability

Further Reading

• Aho, A.V., Hopcroft, J.E. and Ullman, J.D. (1974) The Design and Analysis of Computer Algorithms. Reading, MA: Addison-Wesley.
• Baase, S. and Van Gelder, A. (2000) Computer Algorithms: Introduction to Design and Analysis. 3rd edn. Boston, MA: Addison-Wesley.
• Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C. (2022) Introduction to Algorithms. 4th edn. Cambridge, MA: MIT Press.
• Dijkstra, E.W. (1976) A Discipline of Programming. Englewood Cliffs, NJ: Prentice Hall.
• Goodrich, M.T. and Tamassia, R. (2014) Algorithm Design and Applications. Hoboken, NJ: Wiley.
• Hoare, C.A.R. (1969) ‘An axiomatic basis for computer programming’, Communications of the ACM, 12(10), pp. 576–580.
• Kleinberg, J. and Tardos, É. (2006) Algorithm Design. Boston, MA: Pearson/Addison-Wesley.
• Knuth, D.E. (1997) The Art of Computer Programming, Volume 1: Fundamental Algorithms. 3rd edn. Boston, MA: Addison-Wesley.
• Manber, U. (1989) Introduction to Algorithms: A Creative Approach. Reading, MA: Addison-Wesley.
• Skiena, S.S. (2020) The Algorithm Design Manual. 3rd edn. Cham: Springer.

References

• Aho, A.V., Hopcroft, J.E. and Ullman, J.D. (1974) The Design and Analysis of Computer Algorithms. Reading, MA: Addison-Wesley.
• Baase, S. and Van Gelder, A. (2000) Computer Algorithms: Introduction to Design and Analysis. 3rd edn. Boston, MA: Addison-Wesley.
• Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C. (2022) Introduction to Algorithms. 4th edn. Cambridge, MA: MIT Press.
• Dijkstra, E.W. (1976) A Discipline of Programming. Englewood Cliffs, NJ: Prentice Hall.
• Goodrich, M.T. and Tamassia, R. (2014) Algorithm Design and Applications. Hoboken, NJ: Wiley.
• Hoare, C.A.R. (1969) ‘An axiomatic basis for computer programming’, Communications of the ACM, 12(10), pp. 576–580.
• Kleinberg, J. and Tardos, É. (2006) Algorithm Design. Boston, MA: Pearson/Addison-Wesley.
• Knuth, D.E. (1997) The Art of Computer Programming, Volume 1: Fundamental Algorithms. 3rd edn. Boston, MA: Addison-Wesley.
• Manber, U. (1989) Introduction to Algorithms: A Creative Approach. Reading, MA: Addison-Wesley.
• Skiena, S.S. (2020) The Algorithm Design Manual. 3rd edn. Cham: Springer.

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