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<nav class="sidebar" id="sidebar" aria-label="Kapitelnavigation"><div class="sidebar-inhalt"><details class="sidebar-gruppe"><summary>Einstieg</summary><ul><li data-kapitel="vorwort.html"><a href="vorwort.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Vorwort & Lesehilfe</span></a></li><li data-kapitel="notation.html"><a href="notation.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Notation & Abkürzungen</span></a></li></ul></details><details class="sidebar-gruppe"><summary>Teil I: Grundlagen des Operations Research</summary><ul><li data-kapitel="einfuehrung.html"><a href="einfuehrung.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 1: Einführung in Operations Research — Vom Ursprung zur mathematischen Entscheidungsfindung</span></a></li><li data-kapitel="fundament.html"><a href="fundament.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 2: Das mathematische Fundament — Vektoren, Matrizen, Konvexität</span></a></li><li data-kapitel="oekosystem.html"><a href="oekosystem.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 3: Das Python-Ökosystem für OR — Solver, Bindings und Modellierungsschichten</span></a></li><li data-kapitel="modellierung.html"><a href="modellierung.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 4: Vom Management-Wunsch zum Modell</span></a></li><li data-kapitel="synthese-grundlagen.html"><a href="synthese-grundlagen.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Synthese Teil I</span></a></li></ul></details><details class="sidebar-gruppe"><summary>Teil II: Die Kernverfahren der deterministischen Optimierung</summary><ul><li data-kapitel="lp.html"><a href="lp.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 5: Lineare Programmierung — Simplex, Dualität und Schattenpreise</span></a></li><li data-kapitel="milp.html"><a href="milp.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 6: Gemischt-ganzzahlige Optimierung — Diskrete Entscheidungen und Branch-and-Bound</span></a></li><li data-kapitel="cpsat.html"><a href="cpsat.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 7: Constraint Programming mit CP-SAT — Logik, Scheduling und Zuweisung</span></a></li><li data-kapitel="graphen.html"><a href="graphen.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 8: Graphen, Flüsse und Touren — Min-Cost-Flow, Matching und VRP</span></a></li><li data-kapitel="metaheuristiken.html"><a href="metaheuristiken.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 9: Metaheuristiken — wenn der exakte Solver aussteigt</span></a></li><li data-kapitel="dekomposition.html"><a href="dekomposition.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Kapitel 10: Spaltengenerierung — das Modell umbauen statt die Lösung raten</span></a></li><li data-kapitel="synthese-kernverfahren.html"><a href="synthese-kernverfahren.html"><span class="fortschritt-haken"><svg class="icon" aria-hidden="true"><use href="#icon-check"></use></svg></span><span>Synthese Teil II</span></a></li></ul></details><details class="sidebar-gruppe"><summary>Teil III: Nichtlinearität, Unsicherheit und mehrperiodige D
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<main class="content">
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<nav class="breadcrumb" aria-label="Breadcrumb"><a href="index.html">Start</a> › <span>Anhang F: Literaturverzeichnis</span></nav>
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<nav class="prev-next"><a class="prev-next-knopf prev-next-prev" href="anhang-glossar.html"><svg class="icon" aria-hidden="true"><use href="#icon-chevron-left"></use></svg><span><small>Zurück</small>Anhang E: Glossar</span></a><span class="prev-next-leer"></span></nav>
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<article>
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<h1 id="anhang-literatur">Anhang F: Literaturverzeichnis</h1>
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<h2 id="mathematische-optimierung-grundlagen">Mathematische Optimierung — Grundlagen</h2>
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<ul>
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<li><strong>Bertsimas, D. & Tsitsiklis, J. N. (1997):</strong> <em>Introduction to Linear Optimization.</em> Athena Scientific, Belmont. <em>Standardwerk zur geometrischen und algebraischen Theorie des Simplex-Verfahrens, Dualität, Sensitivitätsanalyse und Netzwerkflüssen.</em></li>
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<li><strong>Dantzig, G. B. (1963):</strong> <em>Linear Programming and Extensions.</em> Princeton University Press. <em>Das historische Originalwerk des Erfinders des Simplex-Algorithmus.</em></li>
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<li><strong>Wolsey, L. A. (2020):</strong> <em>Integer Programming</em> (2. Aufl.). Wiley, Hoboken. <em>Branch-and-Bound, Branch-and-Cut, Polyedertheorie, Formulierungsstärke.</em></li>
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<li><strong>Hillier, F. S. & Lieberman, G. J. (2021):</strong> <em>Introduction to Operations Research</em> (11. Aufl.). McGraw-Hill. <em>Breite Einführung inkl. Warteschlangen, DP und Entscheidungsmodellen.</em></li>
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</ul>
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<h2 id="konvexe-nichtlineare-und-robuste-optimierung">Konvexe, nichtlineare und robuste Optimierung</h2>
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<ul>
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<li><strong>Boyd, S. & Vandenberghe, L. (2004):</strong> <em>Convex Optimization.</em> Cambridge University Press. <em>Referenzwerk für konvexe Mengen, KKT, QP und SDP; theoretische Basis von CVXPY. Frei verfügbar unter stanford.edu/~boyd/cvxbook/.</em></li>
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<li><strong>Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009):</strong> <em>Robust Optimization.</em> Princeton University Press.</li>
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<li><strong>Bertsimas, D. & Sim, M. (2004):</strong> <em>The Price of Robustness.</em> In: <em>Operations Research</em> 52(1), S. 35–53. <em>Quelle des Budgeted-Uncertainty-Ansatzes.</em></li>
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<li><strong>Bellman, R. (1957):</strong> <em>Dynamic Programming.</em> Princeton University Press.</li>
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</ul>
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<h2 id="constraint-programming-und-scheduling">Constraint Programming und Scheduling</h2>
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<ul>
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<li><strong>Rossi, F., van Beek, P. & Walsh, T. (2006):</strong> <em>Handbook of Constraint Programming.</em> Elsevier.</li>
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<li><strong>Perron, L. & Furnon, V.:</strong> <em>OR-Tools CP-SAT Solver Documentation.</em> <a href="https://developers.google.com/optimization/cp">developers.google.com/optimization/cp</a></li>
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</ul>
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<h2 id="quantitative-finanzmathematik">Quantitative Finanzmathematik</h2>
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<ul>
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<li><strong>Markowitz, H. (1952):</strong> <em>Portfolio Selection.</em> In: <em>The Journal of Finance</em> 7(1), S. 77–91.</li>
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<li><strong>Rockafellar, R. T. & Uryasev, S. (2000):</strong> <em>Optimization of Conditional Value-at-Risk.</em> In: <em>Journal of Risk</em> 2(3), S. 21–42.</li>
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<li><strong>Ledoit, O. & Wolf, M. (2003):</strong> <em>Improved estimation of the covariance matrix of stock returns with an application to portfolio selection.</em> In: <em>Journal of Empirical Finance</em> 10(5), S. 603–621. <em>Quelle des Konstant-Korrelations-Ziels.</em></li>
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<li><strong>Ledoit, O. & Wolf, M. (2004):</strong> <em>A well-conditioned estimator for large-dimensional covariance matrices.</em> In: <em>Journal of Multivariate Analysis</em> 88(2), S. 365–411. <em>Quelle des von <code>scikit-learn</code> verwendeten Ziels (skalierte Einheitsmatrix).</em></li>
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<li><strong>Almgren, R. & Chriss, N. (2000):</strong> <em>Optimal Execution of Portfolio Transactions.</em> In: <em>Journal of Risk</em> 3(2), S. 5–39.</li>
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<li><strong>DeMiguel, V., Garlappi, L. & Uppal, R. (2009):</strong> <em>Optimal Versus Naive Diversification.</em> In: <em>Review of Financial Studies</em> 22(5), S. 1915–1953. <em>Die Studie, die 1/N als ernstzunehmenden Vergleichsmaßstab etablierte.</em></li>
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<li><strong>López de Prado, M. (2018):</strong> <em>Advances in Financial Machine Learning.</em> Wiley. <em>Backtest-Overfitting, Deflated Sharpe Ratio, Denoising.</em></li>
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<li><strong>Cornuéjols, G., Peña, J. & Tütüncü, R. (2018):</strong> <em>Optimization Methods in Finance</em> (2. Aufl.). Cambridge University Press.</li>
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</ul>
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<h2 id="software-und-dokumentation">Software und Dokumentation</h2>
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<ul>
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<li><strong>Google OR-Tools</strong> — <a href="https://developers.google.com/optimization">developers.google.com/optimization</a> · Quellcode: <a href="https://github.com/google/or-tools">github.com/google/or-tools</a></li>
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<li><strong>Diamond, S. & Boyd, S. (2016):</strong> <em>CVXPY: A Python-embedded modeling language for convex optimization.</em> In: <em>Journal of Machine Learning Research</em> 17(83), S. 1–5. · <a href="https://www.cvxpy.org">www.cvxpy.org</a></li>
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<li><strong>Huangfu, Q. & Hall, J. A. J. (2018):</strong> <em>Parallelizing the dual revised simplex method.</em> In: <em>Mathematical Programming Computation</em> 10(1), S. 119–142. · <a href="https://highs.dev">highs.dev</a></li>
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<li><strong>SciPy</strong> — <a href="https://docs.scipy.org/doc/scipy/reference/optimize.html">docs.scipy.org/doc/scipy/reference/optimize.html</a></li>
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<li><strong>scikit-learn, Covariance Estimation</strong> — <a href="https://scikit-learn.org/stable/modules/covariance.html">scikit-learn.org/stable/modules/covariance.html</a></li>
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</ul>
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<h2 id="verbände-und-normen">Verbände und Normen</h2>
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<ul>
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<li><strong>INFORMS</strong> — <a href="https://www.informs.org">www.informs.org</a></li>
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<li><strong>GOR (Gesellschaft für Operations Research e. V.)</strong> — deutschsprachiger Fachverband.</li>
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<li><strong>Basel Committee on Banking Supervision:</strong> <em>Minimum capital requirements for market risk</em> (Basel III, FRTB) — Grundlage der Umstellung von VaR auf Expected Shortfall.</li>
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<li><strong>Europäische Union:</strong> MiFID II / ESMA-Leitlinien zum algorithmischen Handel.</li>
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</ul>
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</article>
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<script defer src="assets/programme.js"></script>
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<script defer src="assets/site.js"></script>
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</body>
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