#optimal control

How do variable-cost services, like airlines, stagger their prices against customer demand while ensuring they hit profit margins?

One way is “Optimal Control in Price Decision Making”

Hear us discuss this at the DataCafé ☕️🎧

👉 http://datacafe.buzzsprout.com/1073626/3894179-optimal-control-in-price-decision-making

Silly thoughts on Wednesday:

The phrase "love me more than hate me all the time" from "Lost on You" by LP has potential as a mathematical optimal control problem.

You track two emotions (love and hate) and control your behavior to ensure your love level is always higher than the hate level.

(Maybe you can even use the L^p spaces…)

OPTIMAL CONTROL OF LOWER EXTREMITY MUSCULOSKELETAL MOTION

OPTIMAL CONTROL OF LOWER EXTREMITY MUSCULOSKELETAL MOTION

TABLE OF CONTENT Title page- – – – – – – – – i Approval page – – – – – – – -ii Dedication – – – – – – – – -iii Acknowledgement – – – – – – – -iv Abstract – – – – – – – – – -v Table of content – – – – – – – -vi

CHAPTER ONE INTRODUCTION – – – – – – – -1 1.0 Background of the study – – – – -1 1.1 Statement of the problem – – – – -5 1.2 Purpose…

National oil companies (NOCs) control international oil markets. Nevertheless, by the end of the 2000s, their share of the industry’s total revenues was only 35% while controlling more than 70% of the oil reserves and 65% of the gas reserves. Conventional financial theory prescribes that the proper management of an enterprise should seek the maximization of the NOCs’ profits. However, maximization of profits is not their only objective. Their targets often include non-commercial objectives, such as domestic fuel subsidies and employment. This paper develops a model to assess the impact of domestic fuel subsidies and employment on NOCs’ performance, which clarifies the trade-offs among non-commercial objectives and NOCs’ market value, production, and reinvestment. The model is applied and calibrated to the Colombian NOC to find the financial and operative effects of these non-commercial objectives for different scenarios.

Load frequency control issues in multiarea power system: A Review

by Nazia Kosser" Load frequency control issues in multiarea power system: A Review" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, 

URL: http://www.ijtsrd.com/papers/ijtsrd11650.pdf  

Direct URL: http://www.ijtsrd.com/engineering/electrical-engineering/11650/load-frequency-control-issues-in-multiarea-power-system-a-review/nazia-kosser

open access journal of engineering, ugc approved journals for engineering, call for paper engineering

Student: is the state transition matrix always invertible?

Professor: yes it is always invertible.

[The hardware includes the complete phylogenic set of articulated segments, neuromuscular actuators, sensors, and central neurons available to the species, thereby delimiting its ultimate capabilities, which evolution presumably seeks to optimize. The software includes whatever ontogenic modifications and memories a given individual has managed to accumulate, thereby delimiting its current performance.]

A modern engineer faced with designing, building, and programming a complex system starts first with a set of tasks that it must perform, then figures out the simplest strategies for performing those tasks, then selects the most cost-effective general purpose components that can embody those strategies, and finally assembles the hardware and writes the software as a foregone conclusion. The wise engineer knows that “mission creep” is inevitable, and so he/she designs in as much extra capacity as possible based on currently available “off-the-shelf” technologies, eschewing expensive and risky custom development unless absolutely unavoidable. These stock components themselves represent decades of intensive and expensive development that simultaneously empowers the engineer to achieve a high level of performance at low cost while confining that performance to some arbitrary limits, an acceptable bargain if that performance is “good enough” to meet the original requirements. Inevitably, the product eventually becomes obsolete as newer off-the-shelf technologies emerge (i.e., hardware evolution) and are incorporated into competing products, enabling their programmers to write software that serves new demands that thereby become requirements for competing systems.

A biological organism evolves as an assembly of “off-the-shelf” cellular and molecular components that themselves represent hundreds of millions of years of evolution. These components and the specific organism are optimal only in the sense that their survival indicates that no better designs have yet occurred in the particular ecological niches that they occupy. For most of biological history, performance depended almost entirely on such hardware. The “learning” of primitive organisms is akin to fixed rules for adaptive control that are themselves embodied in the genetic firmware of modulation and messengers, thereby limiting the behavioral repertoire of the organism. At some relatively recent point in evolution, nervous systems became capable of something akin to programming a general purpose computer. It should be noted that the precipitating factor is probably not to be found in the neural hardware itself. The underlying physics of a digital computer are no different from those of a transistor radio, but the emergent properties of those products force us to treat them differently. In particular, the actual performance of the computer depends on both the slow and expensive evolution of its hardware and firmware (now requiring billion dollar silicon foundries) and the rapid and cheap experiments with writing code (suitable for “dotcom” start-up companies). As intelligent biological organisms, humans inherit the best current neural components and system architectures that Mother Nature has yet devised and then spend our countless individual lives hacking together software.

pretty