Last updated on Sep 25, 2024

How do you communicate and present your linear programming findings and recommendations in Matlab?

Linear programming (LP) is a powerful technique for optimizing complex problems with multiple constraints and objectives. Matlab is a popular software for implementing and solving LP models, as well as visualizing and analyzing the results. But how do you communicate and present your LP findings and recommendations in Matlab? In this article, we will cover some tips and best practices for creating clear and compelling reports and presentations using Matlab's built-in tools and features.

1 Define the problem and the solution

The first step in communicating your LP findings and recommendations is to define the problem and the solution in a concise and understandable way. You should explain the main variables, constraints, and objectives of your LP model, and how you formulated and solved it using Matlab. You should also summarize the optimal solution and its key characteristics, such as the optimal value, the optimal point, and the sensitivity analysis. You can use Matlab's command window, editor, or workspace to display and document your code and output.

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Kenneth Chin

Summary of Experiences - U.S. Department of Transportation at FAA/FRA & CNA
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This is Multi-Linear Analysis. We must correct for co-variance between the independent variables.). Use the analysis of residuals. Remember that two planes in parallel space is almost 100% correlated, but maybe very far away from each other, thus causing inaccurate forecasting even if one can prove "cause and effect". The best fit is a low standard deviation, not a high correlation. Using the same sample, one equation has a 99% correlation and a standard deviation of 1,000 miles is deceiving! Remove the autocorrelation, and the adjusted raw data shows a 48% correlation and a standard deviation of just 50 miles. So tell the boss- what you want!! 1,000 miles or just 50 miles off target

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Matthew Brener

Senior Economics Manager at Invenergy LLC
(edited)
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It's also important to explain why an LP technique was selected in the first place as the solution and why it's applicable to the problem. The concepts of optimization and equilibrium are not always intuitively obvious to a non- technical audience and assumptions of linearity while advantageous also are limiting. The implications of the assumptions and conditions are key to an effective description of the utility of the solutions.

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2 Use tables and charts

The next step in communicating your LP findings and recommendations is to use tables and charts to visualize and compare the data and the results. You can use Matlab's built-in functions and commands to create various types of tables and charts, such as matrices, vectors, histograms, bar charts, pie charts, scatter plots, line plots, contour plots, and surface plots. You can also customize the appearance and style of your tables and charts, such as the labels, titles, colors, legends, axes, and annotations. You can use Matlab's figure window, GUI, or app designer to create and display your tables and charts.

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Aayush Patel

HFT Developer @Silverleaf Capital Services || Quantitative Research Consultant @WorldQuant || International Quant Championship Worldwide Gold Level IWC'2024 || Competitive Programmer
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Tables and charts are essential for presenting linear programming results in a way that is both understandable and actionable. MATLAB offers a variety of options for creating visual representations. I typically use bar charts to compare different solutions and line plots to show optimization trends over time. Customizing these visual by adding titles, axes labels, and legends makes the findings more intuitive and professional.

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3 Highlight the insights and implications

The third step in communicating your LP findings and recommendations is to highlight the insights and implications of your analysis. You should interpret and explain the meaning and significance of your tables and charts, and how they relate to the problem and the solution. You should also identify and discuss the main trade-offs, limitations, and assumptions of your LP model, and how they affect the validity and reliability of your results. You can use Matlab's report generator, presentation, or live script to create and present your insights and implications.

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4 Make clear and actionable recommendations

The final step in communicating your LP findings and recommendations is to make clear and actionable recommendations based on your analysis. You should state what actions or decisions should be taken or avoided to achieve the best outcome or improve the current situation. You should also justify your recommendations with evidence and logic, and address any potential challenges or risks that might arise from implementing them. You can use Matlab's report generator, presentation, or live script to create and deliver your recommendations.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you communicate and present your linear programming findings and recommendations in Matlab?

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1 Define the problem and the solution

2 Use tables and charts

3 Highlight the insights and implications

4 Make clear and actionable recommendations

5 Here’s what else to consider

Quantitative Analytics

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