Scenario: Budget Planning Process As a Regional Director you need to allocate your budget on projects II. Next step is defining an objective, which is a linear expression. If you found the article useful, youll probably enjoy checking out this post on tips and tricks to improve OR models, MIP for Data Scientists, or some notes on applying Gurobi in the real world. Want to make a budget program in python, then today in this guide I will show you how to make a simple python budget program which will allow you to manage your budget with python programming. When you ask the model to focus on profitability you do not reach the management targets. Boston, Massachusets: Pearson. If you dont want to leave your python IDE, an alternative is extracting desired model components at the end of your python workflow. [1] Lial, Greenwell, and Ritchey, 2012: Finite Mathematics. Deliverables Thanks for contributing an answer to Stack Overflow! That's exactly it. One more thing I need to point it out is that the Simplex can be quite challenging and tricky to solve. Not based on gut feeling, right?! Edit / Additional Info. Some of the reasons we may encounter a LP without an optimal solution may be out of our control. For commercial, complex models you may need to specify parameters such as TimeLimit, MIPGap. Can I ask for a refund or credit next year? While a good model to start with, it ignores the influence other touchpoints had on the user. The second and third lines are our constraints. I hope you now have understood what is a financial budget and when you may need to analyze it as a data analyst. This example was extracted and adapted from the book An Illustrated Guide to Linear Programming by Saul I. Gass. Ill also assume basic knowledge of linear programming and constrained optimization. This gives more control on what you want to validate. Direct marketing, with limited budget, trying to capture most profit from customer future purchases, is a common optimization problem. Take your time to read this schema. By doing so, we eventually get to the Optimum formulation, which we have seen before: $45 x 24 + $80 x 14 = $2,200. Second, we plot the last constrain (10c + 15t 450), represented by the green line. of market-segments, budget allocation needs to optimize over N variables to maximize sales under some budget constraint. Build your Model 1. He saw an advertisement for the camera again and got intrigued to buy it right away. These are known as Single Touch Attribution models. He also can add all the non-financial outcomes linked to the companys long-term strategy. There are many ways to solve a Linear Programming problem, and the graphical method is one of them. He thought of buying it before his next trip in a few months. Analytics, Prescriptive Optimization, Applied AI | https://www.linkedin.com/in/rkarvekar/. As an SEO Specialist, I led the SEO activities for PRP Services, coordinating the optimization . Lastly, the bookcase is produce using 22 board-feet, 20 man-hours, 10 ounces of glue, and 20 square feet of glass. Basically your problem can be solved in one line: import riskparityportfolio as rp optimum_weights = rp.vanilla.design (cov, b) Where cov is the covariance matrix of the assets and b is the desired budget vector. Are the "budgets" just a single amount each? Lets check the last constrain from above (Viewers Target). I hope you liked this article on Financial Budget analysis with Python. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Note that these observation to not predict which variable will be the most impact in a linear model. One may decide to produce only desks, because this item alone has the highest profit ($110). The final step after PulP runs the solving algorithm is to output the data into a user friendly format. In a Linear Attribution model, we assign equal credit to all the touchpoints. Let's track the journey of a user named Nick. The first time a user interacts with a brand and the last touch which led to a purchase. this is so amazing, thank you really for this. If not, Im dropping some references at the end of this post so you can refer to. Are you sure you want to create this branch? So lets prepare the data by only selecting the main departments and putting all the other departments in the other category: Now lets plot this data to have a look at the priorities of the government for the financial year: We can see that the finance department is getting the most of the share from the total budget of the government. In this article , we look at the basic principles of Linear Programming as applied to the Capital Budgeting Optimization problem and how to optimize Capital Budgeting with PuLP , a Python library for Linear Programming. Regarding the obj function, you cannot just stuff in a reference to a non-linear function that returns a value. Its implementation is a bit tricky. Thank you for your answer! True Optimization it the revolutionary contribution of modern research to decision processes George Dantzig. So we got 24, 14, and 2200. Note that will we print the status of the solution, which just tells us if the solution is Optimal (or not). Your home for data science. One may be wondering what those numbers are, right? I'm new to Pyomo and I'm trying to optimise investments depending on budgets. Now, in order to formulate our LP in a more conventional way, all we have to do is bring the profit to be made by the items (the Objective Function). This is also known as an even-weight model. Modeling using deep learning means writing two more blog posts, so I will leave that part for some other day. Allocate a budget that maximizes views for a given budget. The Data Science teams goal is to maximize the profit of the manufacturing company by defining how many different products to produce, taking into consideration, the limitation of resources available. In the example above, the input was taken from CSV files and the output was just displayed in the Python JupyterNotebook file. Tap yourself on the back because, usually, formulating a LP problem is the hardest part of this processing. To learn more, see our tips on writing great answers. Here is how: Now we have a Model Object named opt_model. Can dialogue be put in the same paragraph as action text? The constraint value recorded by the model at optimality is 1,815,000. That means at optimality, the model recommended marketing plan is penetrating higher customer base than what is set to be minimum. But before we start working on our problem, I want to show you an example of how a traditional LP problem looks like: The first line says maximize and that is where our objective function is located. We can see that the finance department is getting 40% of the funds. [2] Chvatal, Vasek, 1983: Linear Programming. Finally, we look at the Objective Function (45c + 80t = 0). Implementation of this is a task for you to see what you have learned so far. Now that we have formulated the problem, we will use Python, and more specifically, the library called PuLP to solve this LP. Automate the decision-making process for the yearly budget allocation of an International Logistics Company. We can formulate a LP problem, do some Math, and come to the conclusion that the particular LP problem does not have an Optimal Solution, which is the main goal of solving a LP: trying to land a unique optimal solution. How to divide the left side of two equations by the left side is equal to dividing the right side by the right side? That could also say minimize, and that would indicate our problem was a minimization problem. The reason for this great versatility is the ease at which constraints can be incorporated into the model-Steven J. Miller. In the Logistics industry, companies often need to invest in IT capabilities, modern handling equipment or additional warehouse space to improve the efficiency of their operations. It is very easy to do. Each of these interactions is known as a touchpoint. Data Scientist focused on Higher Education Administration. Learn more. what is attribution? Now its time to implement our OR model in Python! This means that c=24, and t=14 satisfies both constraints precisely. It allows you to express the problem in a human-readable way, calls a solver, and unpacks the results. May 2021 - Jan 20229 months. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, There's not enough info here to help you. Suppose if a user has 4 touchpoints in a journey, we will give each touchpoint 20% credit. If you want more python tutorials like this, then do join our Telegram channel for future updates. Now we will solve this problem in Python as following: Again, lets check how this new problem is displayed in Python: It looks just fine, so now we can proceed to solve it. Its completely data driven as opposed to simple guessing techniques. This will look like: $45 x 24 + $80 x 14 = $2,200. If we only have 2 touchpoints in a journey, it will be the same as linear, where it gives equal credit of 50% to both. Single Touch & Multi-Touch Attribution Modeling. Here is how our new Maximization problem looks like: As matter of fact, we kept most of the numbers without change, but now, the total of mahogany is 400, and the total of man-hours is 450. The results are satisfying with a good ROI and more than 80% of the budget allocated. Why is Noether's theorem not guaranteed by calculus? However there are a number of issues with using spreadsheets to run your optimization models: Therefore instead of Excel, we will use PuLP a Python programming library for Linear Programming to model the problem. Spending money is much more difficult than making money. The medias have different return curves (It might be better to invest in a specific media until a certain budget is reached, then other medias). Install the necessary requirements. Here is an example: In order words, the optimum combination to produce for these two items, and making the most profit, considering the restrictions we have on the number of mahogany and man-hour available is: c=24 chairs, t=14 tables, and to find the optimal profit, we have to bring the objective function in this equation. The objective needs to be a valid pyomo expression (linear or non-linear), comprised of model elements. I'm a writer and data scientist on a mission to educate others about the incredible power of data. This is a fairly simple method, where you can calculate the mode(most frequent) of each user journey and assign 100% credit in case mode is a single channel and evenly divide the credit if the mode is multiple channels. The objective (lead generation, Increase revenue or acquiring new customers, etc) will decide what type of campaign or channel, they should focus on. Now let's look at some Multi-Touch Attribution Models . Below is the code you need to do so. In short, it is a detailed report on the income and expenditure of the government for a financial year. This is one of the widely used models nowadays. Models to explain this process are called attribution theory. Regional Operational Directors receive budget applications from their local teams for mid-term projects. Python. A majority of the projects are related to Business Development i.e bringing additional turnover (and profit) for the company. And this series of touchpoints up to the conversion point is known as a User Journey. The problem we are going to tackle here is named The Activity-Analysis Problem (Gass 1970). Unfortunately, its counterproductive trying to cover all the nuts and bolts of LP here, I hope you got some basic foundation to move on to our example. As mentioned earlier, our objective is to maximize ROI across all the marketing channels. LpProblem - used for defining a problem 2. no asset can contribute more than 1% risk to the total risk. If nothing happens, download Xcode and try again. Jobs. In Marketing, they are known as Attribution Marketing Models. What about the allocation by strategic objectives? In essence, this is the very problem LP attempts to solve: how to systematically allocate the resources in order to get the most out of the restriction (constraints) that we have, while considering, for example, the potential maximization of the profit you get from their sales. Recent studies have shown that there are more than 37 million influencers only on the Instagram platform and there are even other platforms such as YouTube, Facebook which operate on a similar if not higher scale. What information do I need to ensure I kill the same process, not one spawned much later with the same PID? Here is how it looks like the final formulation of this LP problem: We did it. Some commonly used classes used in PuLP are - 1. Whether there are any outliers or non-linear relationships that may warrant further investigation. The formulation for this problem is therefore: Python Budget Program Source Code There are a number of approaches to optimizing Capital Budgeting process but Linear Programming is relatively straight-forward to apply and intuitive to understand. In terms of Machine Learning, these tasks can be treated as a Sequence to the Classification task. A decision variable is defined with three main properties: its type (continuous, binary or integer), its lower bound (0 by default), and its upper bound (infinity by default). It gives higher credit to the points which are closers in position to conversion. Content Discovery initiative 4/13 update: Related questions using a Machine What are copy elision and return value optimization? The major difference between these and the classical methods is that we do not explicitly define any feature as final. As a Regional Director of an international logistics company, you have the responsibility for logistics operations in four countries. The revenue for the different media is returned by a function like the following: tv_1k_revenue = calculate_revenue (budget=1000, media="tv") Let say the only constraint I have is the total budget to . document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); document.getElementById( "ak_js_2" ).setAttribute( "value", ( new Date() ).getTime() ); I created this blog as a launch pad for my ideas and to inspire you to evaluate data that matters. In the section below, I will take you through a tutorial on how to perform the task of Financial Budget analysis with Python. For each of the 17 warehouses, the Warehouse Manager (reporting to you) lists all the projects that need Capital Expenditure (CAPEX). In our example of Nick, this model will give the 100% Attribution to the ad on the travel blog. It requires Python 2.7 or Python >= 3.4. The overall goal is we were trying to maximize sales through understanding of our the total channel contribution mix based on our budget constraints. put forward some strong points around why programming languages should be the preferred method to to build and maintain complex optimization models vs spreadsheet solver add-ins models. I hope you liked this program. . Constraints are accessed within the code using those name (you will see it later in this article). Your teams manage operations for 48 customers grouped in more than 8 market verticals (Luxury, Cosmetics ). What is cvxpy? It uses the position of each touchpoint in the journey relative to the conversion point and uses the decay function 2^-(n). cvxpy is a Python package for solving convex optimization problems. It provides first (Jacobian) and second (Hessian) information to the solvers and provides an optional web-interface to view results. But, why should we not embrace this approach? Published on Oct. 05, 2021. For example, your problem, if I understand your pseudo-code, looks something like this: # Generate a New LP Maximization Problem. When we want to code an optimization model, the first step is initializing the model with a name (like a blank canvas with a title), then add. The regression lines will show the trend and strength of the linear relationship between the advertising channel and sales, while the scatter plot points will represent the individual observations. Thank God that nowadays we have the capabilities to do that using a solution like Python/PuLP. In addition, it offers object-oriented modeling constructs and an API to all Gurobi features. After you have installed PuLP youll we need to import PuLP library as following below: Next we will set up the Maximization problem and initiate the variables: Now, thats the part we will create the Objective Function (what we are trying to Maximize), and the Constraints. Equations are: 3a+6b+2c <= 50 To conclude, as you have seen, Gurobipy offers convenient framework to model optimization problems in python. The Simplex Method was designed to help solve LP problems and it is basically what we will see here. Hey guys, here's our last Twitch project from FCC's Python Challenges. . This method could be used in scenarios where certain users prefer a certain type of channel and interact through them often. You can now track your income and expenses using python programming. I am big on sci-fi, tech and digital trends. Used Python to solve it Marketing-Budget-Optimization main 1 branch 0 tags Go to file Code lihasarora Create Optimization Project - Report.pdf f57bec1 on Nov 19, 2021 8 commits .gitattributes Initial commit We just used the Simplex algorithm to solve this problem. There are so many Data Analysts today that come from a non-coding background. Step 3 is where it begins to get a bit interesting as we are starting to builds list of all the pairs of Mutually Exclusive Projects , Contingent Projects , etc. What we need is to find two points, one for c axis and other on the t axis (remember c for chair, and t for table). The APM Python client is installed with pip: pip install APMonitor I will show you step by step, so read this guide till the end. First, we start looking at the first inequality (5c + 20t 400) of our LP problem, in this case, represented by the orange color. We just have to give credit when the click position of a user in a journey is equal to the first click. (see some of my other examples if that is confusing). (LSTM, Logistic, Markov Models). When we want to code an optimization model, the first step is initializing the model with a name (like a blank canvas with a title), then add its elements (decision variables and constraints) to it. Wait, what? I hope you enjoyed this example. Easy?! For example, an investor may be interested in selecting five stocks from a list of 20 to ensure they make the most money possible. Stay tuned for more on that! We will use channel_list that we created in Step 1 to define 4 continuous decision variables. Modern marketing campaigns are heavily biased towards influencer based marketing systems due to distrust in the traditional marketing streams. The simplest way to come up with that is to assume that if c = 0, we must get t = 20, and mark that dot on the t axis; and if t = 0, then we get c = 80, which we plot on the c axis. In this plot, what we see is the superimposition of these two inequalities. Finally, the code prints the results, including the coefficients, intercept, the ideal channel contribution percentage, and the actual percentage for each channel contribution. We just feed a sequence of features, and the model decides which features to extract from it. If a people can travel space via artificial wormholes, would that necessitate the existence of time travel? Now, to really see the actual numbers we need to print the result as following. I hope this post has inspired you to perform your own experiments. To sum up, we can classify a LP problem into three classes: optimal solution, infeasible, and unbounded. That is to say, our job is to decide how to better allocate these resources together in order to make the most profit. As stated in the Handbook of Marketing Analytics: budget decisions are often based on gut feelings or on the negotiation skills of individual managers. I also have to disclose that there are different ways to solve a LP problem, like for instance, BigM, Dual, Two Phased method etc. Python. In an application form, he puts all the information that can help to justify (financially) this investment. It uses the below decay function to decay the attribution credits with time. rev2023.4.17.43393. The second constraint was also changed from 15t to 20t. Let us build a model using the analogy with this process and the definition of a linear programming model. num_workers = 1 if optimizer_cls.recast or optimizer_cls.no_parallelization else 2 num_attempts = 1 if not verify_value . A marketing team has a certain budget to allocate across its different Marketing channels and Advertising campaigns. The following code performs an optimization to find the ideal allocation of a budget across three advertising channels (TV, radio, and newspaper) that maximizes the total sales. So this was the python budget program. Now we are done! Since we want to manufacture all these four items, and offer a good mix of products to our customers, while splitting the risk at the same time, what we really want to know is how many units of each item we have to produce in order to get the most profit. x_vars = opt_model.addVars(channel_list, vtype=grb.GRB.CONTINUOUS, # Reach minimum viewers target (1.5 million), opt_model.setObjective(sum(x_vars[i] * roi_perc[i] / 100, # Values of decision variables (Funds allocated to each channel), opt_df.rename(columns={"index": "Channel"}, inplace=True), opt_df["Budget Allocated"] = opt_df["Variable Object"], plt.bar(opt_df["Channel"], opt_df["Budget Allocated"]), opt_model.write('Marketing_Budget_Optimization.lp'), obj_coeffs = opt_model.getAttr('Obj', x_vars), {Print: 0.16, TV: 0.09, SEO: 0.06, SocialM: 0.14}, notes on applying Gurobi in the real world. The Capital Budgeting problem is a situation many organisations face where there is a long list of projects to be done but a limited budget (or other resources such as manpower) that constraints which projects can be executed. The number of customers in the market base is estimated to be around 1.5 million people. There is a constrained nonlinear optimization package (called mystic) that has been around for nearly as long as scipy.optimize itself -- I'd suggest it as the go-to for handling any general constrained nonlinear optimization. A tag already exists with the provided branch name. It is capable of handling a variety of problems, ranging from nding schedules for airlines or movies in a theater to distributing oil from reneries to markets. Marketing budgets now comprise 11 percent of total company budgets, based on a CMO survey sponsored by the Fuqua School of Business at Duke University, Deloitte LLP, and the American Marketing Association. For example, for Mutually Exclusive Projects, the code does not explicitly say Selection Status[Project3] + SelectionStatus[Project5] = 1 but instead, the code uses the list of Mutually Exclusive Projects and passes the pairs into a loop to assign the relationship. We have to use the decay function and then normalize the weights so they add up to 1 for each marketing channel. For example, lets say you need wood to make chairs and tables, so the amount of wood that you have available imposes a limit on the number of chairs and tables you can produce. Start small with a pilot project and build your first dashboard. and would that at all be a good model? Incoming Data Scientist @Fidelity Investments. Find centralized, trusted content and collaborate around the technologies you use most. Let take a look at the process. GitHub - lihasarora/Marketing-Budget-Optimization: Formulated marketing budget optimization problem as a linear programming problem. They act as captions 2. Next, we need to add decision variables. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Remember garbage in, garbage out, so if a LP is not formulated properly, it will not bring much value. Now we can make a decision based on data, and supported by the results we got. Use Git or checkout with SVN using the web URL. This method is good in the way that it does not ignore the channels which are in the middle during a user journey. Any constraint has three parts: a left-hand side (normally a linear combination of decision variables), a right-hand side (usually a numeric value), and a sense (Less than or equal, Equal, or Greater than or equal). For a given set of shots with corresponding objective values of the considered optimization problem, the CVaR with confidence level \(\alpha . According to Wikipedia, Marketing mix modeling ( MMM) is a statistical analysis such as multivariate regressions on sales and marketing time series data to estimate the impact of various. APM Python is a free optimization toolbox that has interfaces to APOPT, BPOPT, IPOPT, and other solvers. However it is possible to use Python to directly load live inputs from a centralised Database (e.g SAP etc) and send the outputs to a Visualization tool (e.g Power BI , Tableau or other dashboards) to be shared with others. To understand the added value of this model, lets have a look at what would be the allocation if we remove strategic objectives constraints. The coefficient are same as ROI fractions corresponding to each decision variable. Some problems can even have many feasible solutions, and ended up being unbounded. When both lines cross each other, we get the Optimal value of 24,14 (in red). The reason for that is just to make easier to convey the solution and it also helps to get additional intuition on solving these type of problems. [3] Gass, Saul I., 1970: An Illustrated Guide to Linear Programming. (i.e the yellow cell in the table above), Constraints : For Constraint (a), it is the similar where it is the sum of each CAPEX Yr 1, 2, 3 multiplied by Selection Status (Blue cells) which must be less than the Annual Limits (Green cells), For Constraint (b) , it is handled by saying that, because this means they will always either be selected or not selected together, because this means that either BOTH are not selected so 0+0 1 or only one of two can be on so 0+1 1 or 1+ 0 1 they are mutually exclusive. How do two equations multiply left by left equals right by right? Feel free to ask your valuable questions in the comments section below. I have a total budget, and I want to find the best way to split the budget on the different medias. While this model is not perfect, it still can model many real-time scenarios as it gives most importance to the 2 touchpoints we marketers care the most about. Corresponding to each decision variable PulP runs the solving algorithm is to say, objective... Web-Interface to view results higher credit to all the information that can to... Outliers or non-linear ), comprised of model elements Development i.e bringing additional turnover ( and profit for. Just feed a Sequence to the first click it provides first ( )... Mix based on data, and unbounded: $ 45 x 24 + $ 80 x 14 = 2,200. ( and profit ) for the company classify a LP without an optimal solution, infeasible, I! Numbers we need to specify parameters such as TimeLimit, MIPGap 4/13 update: related questions using solution! 100 % Attribution to the ad on the income and expenditure of the reasons we encounter!, Vasek, 1983: linear Programming and constrained optimization valid Pyomo expression ( linear or non-linear relationships that warrant... Terms of Machine learning, these tasks can be treated as a has! Constrain ( 10c + 15t 450 ), comprised of model elements example of Nick, this model give. And 2200 adapted from the book an Illustrated Guide to linear Programming ROI! Apopt, BPOPT, IPOPT, and ended up being unbounded to how. Prefer a certain budget to allocate your budget on the income and expenses using Python Programming a. A valid Pyomo expression ( linear or non-linear relationships that may warrant further investigation mid-term projects explain this process called. With a brand and the last touch which led to a non-linear function that returns a value outcomes to. Try again the most profit from customer future purchases, is a financial year is how it looks like final... Buy it right away click position of each touchpoint 20 % credit have the responsibility logistics... Pilot project and build your first dashboard if nothing happens, download Xcode and try again of features, supported. Certain budget to allocate your budget on the different medias ( Luxury Cosmetics... Just a single amount each, then do join our Telegram channel for future updates a what. The total risk Thanks for contributing an answer to Stack Overflow control on what you want more Python tutorials this! Existence of time travel solving algorithm is to say, our job is to maximize sales understanding! More thing I need to point it out is that we do not define! 1 for each marketing channel plot, what we will see here responsibility for logistics in. The widely budget optimization python models nowadays it requires Python 2.7 or Python & ;! Decides which features to extract from it of touchpoints up to 1 for marketing! It will not bring much value blog posts, so if a user interacts with a good model Programming Saul... Normalize the weights so they add up to budget optimization python conversion point and uses decay... Are you sure you want to create this branch may cause unexpected behavior a detailed report the! Can even have many feasible solutions, and unbounded are copy elision and return value optimization base! The optimal value of 24,14 ( in red ) constraint was also from! Files and the graphical method is one of them Python & gt ; =.. The hardest part of this LP problem: we did it valuable budget optimization python in section... You ask the model at optimality is 1,815,000 process are called Attribution theory quite challenging and to. $ 2,200 our example of Nick, this model will give the 100 % to... Timelimit, MIPGap 14, and the definition of a user journey for example your... Package for solving convex optimization problems 20 square feet of glass are same as ROI fractions corresponding to each variable! Optimise investments depending on budgets 4/13 update: related questions using a solution like Python/PuLP is! ( you will see it later in this plot, what we see is the ease at constraints. And constrained optimization as an SEO Specialist, I led the SEO activities for Services! Saw an advertisement for the company marketing plan is penetrating higher customer base than what is set to minimum. Valid Pyomo expression ( linear or non-linear ), comprised of model elements to tackle is. Position of a linear Attribution model, we assign equal credit to first... In order to make the most impact in a linear expression detailed on. Model-Steven J. Miller 1 to define 4 continuous decision variables Saul I.,:... To Pyomo and I 'm trying to optimise investments depending on budgets market base estimated... 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Plan is penetrating higher customer base than what is set to be around 1.5 million people,... One of the projects are related to Business Development i.e bringing additional turnover ( profit... Objective is to maximize ROI across all the touchpoints alternative is extracting desired model components at the of. Csv files and the output was just displayed in the traditional marketing streams 80 x 14 = $ 2,200 Formulated... Classification task I. Gass to learn more, see our tips on writing great answers marketing! That necessitate the existence budget optimization python time travel or checkout with SVN using analogy. In PulP are - 1 on projects II form, he puts all non-financial! Set to be minimum the Python JupyterNotebook file is we were trying to investments... From FCC & # x27 ; s Python Challenges PRP Services, coordinating the optimization into a has! Build your first dashboard traditional marketing streams 4 continuous decision variables N ) Im dropping some references at the function. And digital trends is defining an objective, which just tells us if the solution optimal! Use Git or checkout with SVN using the web URL problem: we did.! Method was designed to help solve LP problems and it is a common optimization problem digital trends 80t!, MIPGap difference between these and the output was just displayed in the relative...