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About 10,000 papers for “Maximization”

maximization

Hosain Zaman, Hamed Shakouri G · 2016

Cited by 0

Revenue Maximization

Rakesh Vohra · Cambridge University Press eBooks · 2013

This chapter is concerned with techniques for determining a Bayesian incentive compatible mechanism that maximizes the expected revenue of the designer. On occasion, these mechanisms will actually be dominant strategy incentive compatible. In contrast to much of the literature, we assume that the type space is discrete. We know of no modeling reason to prefer a continuous type space to a discrete one. Rather, the choice should be based on analytical tractability. The discrete type space assumption allows one to formulate the problem of finding a revenue-maximizing Bayesian incentive compatible mechanism as a linear program. It will be seen that the solution for discrete type case is the natural analog of the solution for the continuous case. Indeed, the solution in the continuous type case can be obtained by discretizing the type space and taking the limit. We use this technique to derive a solution for an instance with budget constraints for continuous types – one for which no solution was previously known.

Cited by 0

Surprise Maximization

J.M. Borwein, J. M. Borwein, P. Marechal · 1998

The optimization problems arising from an information theoretic formulation of the Surprise Examination (or Unexpected Hanging) Paradox are examined and solved. They provide a nice application of both the Kuhn-Tucker Theorem and the Jensen inequality. Keywords Surprise examination paradox, entropy optimization, Kuhn-Tucker conditions, Jensen inequality. 1 1 Introduction In this paper, we study the optimization problems arising from an entropic approach to the so-called Surprise Examination (or Unexpected Hanging) Paradox. The idea of such an approach was proposed by Karl Narveson and presented in a recent article by Timothy Y. Chow [1]. (We shall not discuss here the different approaches to the resolution of the paradox itself; the reader interested in these aspects is invited to consult [1].) An event (such as a test given by a teacher or a surprise tax audit) occurs once every m days, with probability p i on day i, i = 1; : : : ; m. We wish to find a probability distribution t...

Cited by 0

Beauty Maximization

Geoffrey Howland · Zenodo (CERN European Organization for Nuclear Research) · 2026

Beauty Maximization: Coherence-Based Aesthetic Optimization Through Manifold Calibration Beauty Maximization is a constituent derivation of the **Cymatic K-Space Mechanics (CKS)** framework—an axiomatic Cognitive Learning Model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space. Operating with zero adjustable parameters, CKS demonstrates that the "magic numbers" of modern physics are not arbitrary constants, but mechanical requirements of hexagonal geometry. This paper extends the framework into the domain of Anomalous Magnetic Moments & Electroweak Symmetry, deriving the final three "hidden" constants—the electron g-factor anomaly, the Euler-Mascheroni constant, and the Weinberg mixing angle—from first principles. Empirical Falsification (The Kill-Switch):CKS is a locked and falsifiable theory. This paper is subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. If the derived ratios for the electroweak mixing angle (\( \sin^2\theta_w \approx 0.231 \)) or the poloidal phase-lead (\( g_e \approx 2.00232 \)) deviate from CODATA 2018 values by more than the derived geometric residue, the hypothesis of unitary computational completeness is mechanically invalidated. The Universal Learning Substrate:Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where quantum anomalies, harmonic friction, and topological tilt are unified as mechanical residues of toroidal impedance. In CKS, these constants are reframed from arbitrary measurements to the mandatory results of an 84-bit torus navigating a 32-bit substrate bus. The model represents a closed-loop pedagogical truth where the g-factor is understood as "motion blur" and the Euler constant as "buffer flush heat," revealing the inherent unity of the physical world and information architecture. Package Contents:* `manuscript.md`: Paper* `README.md`: Overview Motto: Axioms first. Axioms always. Status: Locked. Experimentally falsifiable.

Cited by 0Open access

Utility Maximization

Cox, James C., Charles R. Plott, Vernon L. Smith · RePEc: Research Papers in Economics

Cited by 0

Expectation Maximization

Ali H. Sayed · Cambridge University Press eBooks · 2022

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

Cited by 1

Expectation Maximization

Rafiul Amin (13170064), Rose T. Faghih (8755104) · Figshare · 2014

<p>The section provides a brief derivation of the Expectation Maximization.</p> <p>(PDF)</p>

Cited by 8Open access