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Optimization Across Disciplines: A Transdisciplinary Field Guide to Better Choices, Designs, Systems, and Works - Couverture souple
Synopsis
We can think of optimization as choosing among alternatives under constraints. Similar structures appear in different disciplines, although the objects, criteria, and meanings may differ. For example, allocating computing resources, selecting a medical intervention, and revising a musical composition involve different interpretations of what constitutes an improvement.
In Optimization Across Disciplines, we examine these connections across mathematics, computing, engineering, living systems, society, and creative work. We begin with fundamental concepts and proceed to methods and worked examples. These include black-box optimization, where we evaluate candidate solutions without direct access to the objective's internal structure. We also use category theory to examine composition and the conditions under which a relationship can be carried from one domain to another.
The book includes illustrations, method cards, a glossary, an index, and references for further study. A mathematics companion develops the prerequisites from school foundations to advanced topics. An equation atlas connects the formulas in the main text with explanations of their symbols and meaning.
Throughout the book, we consider how objectives are chosen, what evidence supports a result, and what a model leaves out. We also examine situations where further optimization is inappropriate, including when goals change, measurements distort behavior, or important values cannot be represented by a single score.
In a bonus article, we consider optimization in software diagnostics, observability, memory dump analysis, trace and log analysis, and debugging. Optimization applies to both software behavior and the process used to explain it. We may collect more data, run additional debugger commands, or introduce another measurement, but each action has a cost. Its usefulness depends on the diagnostic question and the decision it may change.
Using analysis patterns, we examine evidence selection, competing mechanisms, and controlled intervention. Memory dumps and execution traces provide different projections of software execution, and their combined evidence may answer questions that either artifact leaves unresolved. We introduce a formal model based on expected decision loss and value of information, then relate it to practical choices about instrumentation, sampling, artifact acquisition, and analysis.
A worked latency example follows the process from initial hypotheses to a reversible software change. We also consider incomplete evidence, measurement effects, reduced failure reproductions, and conditions for stopping an investigation. Throughout the article, we retain the assumptions and uncertainty behind each conclusion and specify what a proposed improvement must preserve.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l'auteur
Dmitry Vostokov is an internationally recognized expert, speaker, educator, scientist, inventor, and author. He founded the pattern-oriented software diagnostics, forensics, and prognostics discipline (Systematic Software Diagnostics) and Software Diagnostics and Observability Institute (DA+TA: DumpAnalysis.org + TraceAnalysis.org). Vostokov has also authored over 50 books on software diagnostics, anomaly detection and analysis, software and memory forensics, root cause analysis and problem solving, memory dump analysis, debugging, software trace and log analysis, reverse engineering, and malware analysis. He has over 30 years of experience in software architecture, design, development, and maintenance in various industries, including leadership, technical, and people management roles. Dmitry founded OpenTask Iterative and Incremental Publishing (OpenTask.com) and Software Diagnostics Technology and Services (former Memory Dump Analysis Services) PatternDiagnostics.com. In his spare time, he explores Software Narratology, Software Pathology, Software Morphology, Quantum Software Diagnostics, and Pattern-Oriented AI. His interest areas are theoretical software diagnostics and its mathematical and computer science foundations, application of formal logic, semiotics, artificial intelligence, machine learning, and data mining to diagnostics and anomaly detection, software diagnostics engineering and diagnostics-driven development, diagnostics workflow and interaction. Recent interest areas also include functional programming, cloud native computing, monitoring, observability, visualization, security, automation, applications of category theory to software diagnostics, development and big data, and diagnostics of artificial intelligence.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Optimization Across Disciplines
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Optimization Across Disciplines (Paperback)
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Paperback. Etat : new. Paperback. We can think of optimization as choosing among alternatives under constraints. Similar structures appear in different disciplines, although the objects, criteria, and meanings may differ. For example, allocating computing resources, selecting a medical intervention, and revising a musical composition involve different interpretations of what constitutes an improvement.In Optimization Across Disciplines, we examine these connections across mathematics, computing, engineering, living systems, society, and creative work. We begin with fundamental concepts and proceed to methods and worked examples. These include black-box optimization, where we evaluate candidate solutions without direct access to the objective's internal structure. We also use category theory to examine composition and the conditions under which a relationship can be carried from one domain to another.The book includes illustrations, method cards, a glossary, an index, and references for further study. A mathematics companion develops the prerequisites from school foundations to advanced topics. An equation atlas connects the formulas in the main text with explanations of their symbols and meaning.Throughout the book, we consider how objectives are chosen, what evidence supports a result, and what a model leaves out. We also examine situations where further optimization is inappropriate, including when goals change, measurements distort behavior, or important values cannot be represented by a single score.In a bonus article, we consider optimization in software diagnostics, observability, memory dump analysis, trace and log analysis, and debugging. Optimization applies to both software behavior and the process used to explain it. We may collect more data, run additional debugger commands, or introduce another measurement, but each action has a cost. Its usefulness depends on the diagnostic question and the decision it may change.Using analysis patterns, we examine evidence selection, competing mechanisms, and controlled intervention. Memory dumps and execution traces provide different projections of software execution, and their combined evidence may answer questions that either artifact leaves unresolved. We introduce a formal model based on expected decision loss and value of information, then relate it to practical choices about instrumentation, sampling, artifact acquisition, and analysis.A worked latency example follows the process from initial hypotheses to a reversible software change. We also consider incomplete evidence, measurement effects, reduced failure reproductions, and conditions for stopping an investigation. Throughout the article, we retain the assumptions and uncertainty behind each conclusion and specify what a proposed improvement must preserve. We examine optimization across disciplines, from computing and engineering to medicine and creative work, using worked examples and explanations of the mathematics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781919135007
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Optimization Across Disciplines: A Transdisciplinary Field Guide to Better Choices, Designs, Systems, and Works
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Optimization Across Disciplines (Paperback)
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Paperback. Etat : new. Paperback. We can think of optimization as choosing among alternatives under constraints. Similar structures appear in different disciplines, although the objects, criteria, and meanings may differ. For example, allocating computing resources, selecting a medical intervention, and revising a musical composition involve different interpretations of what constitutes an improvement.In Optimization Across Disciplines, we examine these connections across mathematics, computing, engineering, living systems, society, and creative work. We begin with fundamental concepts and proceed to methods and worked examples. These include black-box optimization, where we evaluate candidate solutions without direct access to the objective's internal structure. We also use category theory to examine composition and the conditions under which a relationship can be carried from one domain to another.The book includes illustrations, method cards, a glossary, an index, and references for further study. A mathematics companion develops the prerequisites from school foundations to advanced topics. An equation atlas connects the formulas in the main text with explanations of their symbols and meaning.Throughout the book, we consider how objectives are chosen, what evidence supports a result, and what a model leaves out. We also examine situations where further optimization is inappropriate, including when goals change, measurements distort behavior, or important values cannot be represented by a single score.In a bonus article, we consider optimization in software diagnostics, observability, memory dump analysis, trace and log analysis, and debugging. Optimization applies to both software behavior and the process used to explain it. We may collect more data, run additional debugger commands, or introduce another measurement, but each action has a cost. Its usefulness depends on the diagnostic question and the decision it may change.Using analysis patterns, we examine evidence selection, competing mechanisms, and controlled intervention. Memory dumps and execution traces provide different projections of software execution, and their combined evidence may answer questions that either artifact leaves unresolved. We introduce a formal model based on expected decision loss and value of information, then relate it to practical choices about instrumentation, sampling, artifact acquisition, and analysis.A worked latency example follows the process from initial hypotheses to a reversible software change. We also consider incomplete evidence, measurement effects, reduced failure reproductions, and conditions for stopping an investigation. Throughout the article, we retain the assumptions and uncertainty behind each conclusion and specify what a proposed improvement must preserve. We examine optimization across disciplines, from computing and engineering to medicine and creative work, using worked examples and explanations of the mathematics. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9781919135007
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Optimization Across Disciplines (Paperback)
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Paperback. Etat : new. Paperback. We can think of optimization as choosing among alternatives under constraints. Similar structures appear in different disciplines, although the objects, criteria, and meanings may differ. For example, allocating computing resources, selecting a medical intervention, and revising a musical composition involve different interpretations of what constitutes an improvement.In Optimization Across Disciplines, we examine these connections across mathematics, computing, engineering, living systems, society, and creative work. We begin with fundamental concepts and proceed to methods and worked examples. These include black-box optimization, where we evaluate candidate solutions without direct access to the objective's internal structure. We also use category theory to examine composition and the conditions under which a relationship can be carried from one domain to another.The book includes illustrations, method cards, a glossary, an index, and references for further study. A mathematics companion develops the prerequisites from school foundations to advanced topics. An equation atlas connects the formulas in the main text with explanations of their symbols and meaning.Throughout the book, we consider how objectives are chosen, what evidence supports a result, and what a model leaves out. We also examine situations where further optimization is inappropriate, including when goals change, measurements distort behavior, or important values cannot be represented by a single score.In a bonus article, we consider optimization in software diagnostics, observability, memory dump analysis, trace and log analysis, and debugging. Optimization applies to both software behavior and the process used to explain it. We may collect more data, run additional debugger commands, or introduce another measurement, but each action has a cost. Its usefulness depends on the diagnostic question and the decision it may change.Using analysis patterns, we examine evidence selection, competing mechanisms, and controlled intervention. Memory dumps and execution traces provide different projections of software execution, and their combined evidence may answer questions that either artifact leaves unresolved. We introduce a formal model based on expected decision loss and value of information, then relate it to practical choices about instrumentation, sampling, artifact acquisition, and analysis.A worked latency example follows the process from initial hypotheses to a reversible software change. We also consider incomplete evidence, measurement effects, reduced failure reproductions, and conditions for stopping an investigation. Throughout the article, we retain the assumptions and uncertainty behind each conclusion and specify what a proposed improvement must preserve. We examine optimization across disciplines, from computing and engineering to medicine and creative work, using worked examples and explanations of the mathematics. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781919135007
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Optimization Across Disciplines: A Transdisciplinary Field Guide to Better Choices, Designs, Systems, and Works
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Optimization Across Disciplines : A Transdisciplinary Field Guide to Better Choices, Designs, Systems, and Works
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Taschenbuch. Etat : Neu. Neuware - We can think of optimization as choosing among alternatives under constraints. Similar structures appear in different disciplines, although the objects, criteria, and meanings may differ. For example, allocating computing resources, selecting a medical intervention, and revising a musical composition involve different interpretations of what constitutes an improvement.In Optimization Across Disciplines, we examine these connections across mathematics, computing, engineering, living systems, society, and creative work. We begin with fundamental concepts and proceed to methods and worked examples. These include black-box optimization, where we evaluate candidate solutions without direct access to the objective's internal structure. We also use category theory to examine composition and the conditions under which a relationship can be carried from one domain to another.The book includes illustrations, method cards, a glossary, an index, and references for further study. A mathematics companion develops the prerequisites from school foundations to advanced topics. An equation atlas connects the formulas in the main text with explanations of their symbols and meaning.Throughout the book, we consider how objectives are chosen, what evidence supports a result, and what a model leaves out. We also examine situations where further optimization is inappropriate, including when goals change, measurements distort behavior, or important values cannot be represented by a single score.In a bonus article, we consider optimization in software diagnostics, observability, memory dump analysis, trace and log analysis, and debugging. Optimization applies to both software behavior and the process used to explain it. We may collect more data, run additional debugger commands, or introduce another measurement, but each action has a cost. Its usefulness depends on the diagnostic question and the decision it may change.Using analysis patterns, we examine evidence selection, competing mechanisms, and controlled intervention. Memory dumps and execution traces provide different projections of software execution, and their combined evidence may answer questions that either artifact leaves unresolved. We introduce a formal model based on expected decision loss and value of information, then relate it to practical choices about instrumentation, sampling, artifact acquisition, and analysis.A worked latency example follows the process from initial hypotheses to a reversible software change. We also consider incomplete evidence, measurement effects, reduced failure reproductions, and conditions for stopping an investigation. Throughout the article, we retain the assumptions and uncertainty behind each conclusion and specify what a proposed improvement must preserve. N° de réf. du vendeur 9781919135007
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